Pseudotime Sexual Branch
Gametocyte Development in Plasmodium berghei
Andrew Russell
February 28, 2021
1. Introduction and Aims
We have merged the two datasets together in GCSKO_merge.Rmd. We also subsetted out the pre-sexual-branch and the sexual cells (male and female) and stored them in a Seurat object called tenx.mutant.integrated.sex. Here, we will perform pseudotime analysis on the dataset and build modules of genes that show similar expression across this pseudotime.
2. Read in the data
Load/Install the Required Packages
[1] "patchwork is loaded correctly"
[1] "viridis is loaded correctly"
[1] "Seurat is loaded correctly"
[1] "cowplot is loaded correctly"
[1] "gridExtra is loaded correctly"
[1] "grid is loaded correctly"
[1] "Hmisc is loaded correctly"
[1] "reshape2 is loaded correctly"
[1] "dplyr is loaded correctly"
[1] "circlize is loaded correctly"
[1] "monocle3 is loaded correctly"
[1] "destiny is loaded correctly"load data
## load sex only branch cells saved from GCSKO_Sex_Branch_Analysis.Rmd
## Restore the objects
## load sex branch dataset
tenx.mutant.integrated.sex <- readRDS("../data_to_export/tenx.mutant.integrated.sex.RDS")
## load full dataset
#tenx.mutant.integrated <- readRDS("../data_to_export/tenx.mutant.integrated.RDS")## add old pt values
## these values are calculated in hte GCSKO_pseudotime_allcells.Rmd script and so were added after the object was split into sex only.
tenx.all <- readRDS("../data_to_export/tenx.mutant.integrated.RDS")
tenx.all.meta <- as.data.frame(tenx.all@meta.data)
tenx.all.meta <- tenx.all.meta[which(rownames(tenx.all.meta) %in% rownames(tenx.mutant.integrated.sex@meta.data)),]
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, tenx.all.meta$old_pt_values, col.name = "old_pt_values")
## then remove these objects so they don't use up memory
rm(tenx.all, tenx.all.meta)Add a meta.data column so that 10X is listed as WT:
## get cells that are filtered out
mutant_cells <- which(tenx.mutant.integrated.sex$experiment == "mutants")
## make extra column in plotting df
tenx.mutant.integrated.sex@meta.data$identity_name_combined <- "WT_10X"
tenx.mutant.integrated.sex@meta.data$identity_name_combined[mutant_cells] <- tenx.mutant.integrated.sex@meta.data$identity_name_updated[mutant_cells]
## check
table(tenx.mutant.integrated.sex@meta.data$identity_name_combined)
fd1 fd2 fd3 fd4 gd1 md1 md2 md3 md4 md5 wild-type WT_10X
91 119 41 69 98 137 209 36 136 154 428 1494 ## create a list of our mutant gene IDs
list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")Read in gene annotations
gene_annotations <- read.table("../data/Reference/GenesByTaxon_Summary.csv", header = TRUE, sep = ",", stringsAsFactors = TRUE)
dim(gene_annotations)[1] 5254 73. Dimensionalty Reduction
We will now re-calculate the UMAP, PCA and diffusion map to visualise the data since the old visualisation used the variation in the whole dataset and so some of the variation in this set of cells was obscured.
ref: https://github.com/satijalab/seurat/issues/1883
A. Recalculate PCA
The PCA is used as the basis of other dimensionality reductions so we will now recalculate this to get to our final UMAP.
First, run PCA again
tenx.mutant.integrated.sex <- RunPCA(tenx.mutant.integrated.sex, npcs = 30, verbose = FALSE)Then inspect the PCs
ElbowPlot(tenx.mutant.integrated.sex, ndims = 30, reduction = "pca")
Have a quick look at the output
FeaturePlot(tenx.mutant.integrated.sex, reduction = "pca", pt.size = 0.01, features = "old_pt_values")
Run tSNE
tenx.mutant.integrated.sex <- RunTSNE(tenx.mutant.integrated.sex, seed.use = 1234, perplexity = 200)
FeaturePlot(tenx.mutant.integrated.sex, reduction = "tsne", pt.size = 0.01, features = "old_pt_values")
B. UMAP
calculate UMAP
tenx.mutant.integrated.sex <- RunUMAP(tenx.mutant.integrated.sex, reduction = "pca", dims = 1:11, seed.use = 1234, n.neighbors = 10, reduction.name = "umapoptimised_post_repca", reduction.key='umap_post_repca_')
23:25:08 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
23:25:08 Read 3012 rows and found 11 numeric columns
23:25:08 Using Annoy for neighbor search, n_neighbors = 10
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
23:25:08 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:25:09 Writing NN index file to temp file /var/folders/7t/kvh8b3952x9_4b3r74r1kstc000glh/T//RtmpM7sRcV/file12d73da38d69
23:25:09 Searching Annoy index using 1 thread, search_k = 1000
23:25:09 Annoy recall = 100%
23:25:11 Commencing smooth kNN distance calibration using 1 thread
23:25:13 Initializing from normalized Laplacian + noise
23:25:13 Commencing optimization for 500 epochs, with 40010 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:25:18 Optimization finished
Keys should be one or more alphanumeric characters followed by an underscore, setting key from umap_post_repca_ to umappostrepca_All keys should be one or more alphanumeric characters followed by an underscore '_', setting key to umappostrepca_check markers to orientate
plots <- FeaturePlot(tenx.mutant.integrated.sex, features = c("PBANKA-1319500", "PBANKA-0416100"), blend = TRUE, combine = FALSE, coord.fixed = TRUE, reduction = "umapoptimised_post_repca")
plots[[3]] + NoLegend() # Get just the co-expression plot, built-in legend is meaningless for this plot
plots[[4]] # Get just the key
CombinePlots(plots[3:4], legend = 'none', ncol =2, nrow = 1, rel_widths = c(2, 1), rel_heights = c(4,1)) # Stitch the co-expression and key plots togetherCombinePlots is being deprecated. Plots should now be combined using the patchwork system.
inspect mutants in 13 that are in between sexes
HoverLocator(plot = umap_mutant, information = FetchData(mutant_only_seurat, vars = c("ident", "identity_updated", "nFeature_RNA", "identity_name_combined")))
the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used`error_y.color` does not currently support multiple values.`error_x.color` does not currently support multiple values.`line.color` does not currently support multiple values.The titlefont attribute is deprecated. Use title = list(font = ...) instead.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used`error_y.color` does not currently support multiple values.`error_x.color` does not currently support multiple values.`line.color` does not currently support multiple values.The titlefont attribute is deprecated. Use title = list(font = ...) instead.DimPlot(mutant_only_seurat, label = TRUE, label.size = 8, repel = FALSE, pt.size = 0.5, dims = c(2,1), reduction = "DIM_UMAP") +
coord_fixed() +
theme(legend.position="bottom",
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank()) +
guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))
4. Clustering
Investigate sub clustering of pre-branch clusters
Ultimately, we are looking for coexpression after the expression of AP2G:
## plot
marker_gene_plot_AP2G <- FeaturePlot(tenx.mutant.integrated.sex, features = "PBANKA-1437500", coord.fixed = TRUE, min.cutoff = "q1", dims = c(2,1), reduction = "DIM_UMAP", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("AP2G Expression")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.##view
marker_gene_plot_AP2G
and we have the following clusters currently:
## Plot
umap_cluster <- DimPlot(tenx.mutant.integrated.sex, label = TRUE, label.size = 8, repel = FALSE, pt.size = 0.5, dims = c(2,1), reduction = "DIM_UMAP") +
coord_fixed() +
theme(legend.position="bottom",
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank()) +
guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))
## print
umap_cluster
11 is the common sex branch so we will interrogate that branch
## subset cluster 13 cells
cells_bipot <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$seurat_clusters == 11 & tenx.mutant.integrated.sex@meta.data$genotype_combined == "WT", ])
## subset cluster 13
seurat_bipot <- subset(tenx.mutant.integrated.sex, cells = cells_bipot)
seurat_bipotAn object of class Seurat
10116 features across 192 samples within 2 assays
Active assay: integrated (5018 features, 2000 variable features)
1 other assay present: RNA
4 dimensional reductions calculated: pca, umap, DIM_UMAP, umapoptimised_post_repca## re-PCA
seurat_bipot <- RunPCA(seurat_bipot, npcs = 30, verbose = FALSE)
ElbowPlot(seurat_bipot, ndims = 30, reduction = "pca")
## recluster
seurat_bipot <- FindNeighbors(seurat_bipot, dims = 1:15)Computing nearest neighbor graph
Computing SNNseurat_bipot <- FindClusters(seurat_bipot, resolution = 1, random.seed = 42, algorithm = 2)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 192
Number of edges: 10432
Running Louvain algorithm with multilevel refinement...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.2651
Number of communities: 3
Elapsed time: 0 secondsDimPlot(seurat_bipot, reduction = "pca", dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.1") 
## plot cluster resolution = 1
## plot
DimPlot(seurat_bipot, reduction = "DIM_UMAP", dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.1") +
coord_fixed() +
theme_void() +
theme(legend.position = "bottom") +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
2 and 1 are superimposed on the branch, lets find markers for each cluster
seurat_bipot_markers <- FindAllMarkers(seurat_bipot, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25, test.use = "MAST")
Calculating cluster 0
Assuming data assay in position 1, with name et is log-transformed.
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...Specifically look for markers between 0 and 2
cluster_1_2_markers <- FindMarkers(seurat_bipot, ident.1 = 0, ident.2 = 2, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s cluster_1_2_markers_subset <- cluster_1_2_markers %>% filter(p_val_adj < 0.05)
cluster_1_2_markers_subset <- cluster_1_2_markers_subset[order(-cluster_1_2_markers_subset$avg_logFC), ]
cluster_1_2_markers_subsettable(seurat_bipot$seurat_clusters, seurat_bipot$identity_combined)
WT WT_10X
0 1 78
1 1 69
2 0 43Do the same analysis on cluster 5 quickly
## find markers
cells_three_markers <- FindAllMarkers(cells_three, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
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|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~00s
|++ | 3 % ~00s
|+++ | 4 % ~00s
|+++ | 6 % ~00s
|++++ | 7 % ~00s
|+++++ | 9 % ~00s
|+++++ | 10% ~00s
|++++++ | 11% ~00s
|+++++++ | 13% ~00s
|++++++++ | 14% ~00s
|++++++++ | 16% ~00s
|+++++++++ | 17% ~00s
|++++++++++ | 19% ~00s
|++++++++++ | 20% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 23% ~00s
|+++++++++++++ | 24% ~00s
|+++++++++++++ | 26% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++ | 29% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 37% ~00s
|++++++++++++++++++++ | 39% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
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|++++++++++++ | 23% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
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|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
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|++++++++++++++++++++++ | 44% ~01s
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|++++++++++++++++++++++++ | 48% ~01s
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|+++++++++++++++++++++++++ | 50% ~01s
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|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
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|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s ## inspect result
markers_subset <- cells_three_markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_logFC) %>% filter(p_val_adj < 0.05)
markers_subset <- markers_subset[order(-markers_subset$avg_logFC), ]
markers_subset
## find markers
markers_subset_mutant <- markers_subset[which(markers_subset$gene %in% list_of_mutant_genes), ]
markers_subset_mutant
## plot
VlnPlot(cells_three, features = c(markers_subset_mutant$gene, "PBANKA-1437500"), assay = "RNA")
Cluster 2 contains the male cells as this has md1 and ap2g in it.
cluster 3 has histone h2b, histone 4, actin 1, so likley contains asexual cells
cluster 0 is potentially female as these genes have female-biased expression in the kasia data
cluster 1 genes were not screened so unknown identity
Then look at the co-expression of AP2-G and MD1
Do the same on cluster 7
## subset cluster 13 cells
cells_seven <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$seurat_clusters == 7 & tenx.mutant.integrated.sex@meta.data$genotype_combined == "WT", ])
## subset cluster 13
cells_seven <- subset(tenx.mutant.integrated.sex, cells = cells_bipot)
cells_sevenAn object of class Seurat
10116 features across 192 samples within 2 assays
Active assay: integrated (5018 features, 2000 variable features)
1 other assay present: RNA
5 dimensional reductions calculated: pca, umap, DIM_UMAP, umapoptimised_post_repca, tsne## re-PCA
cells_seven <- RunPCA(cells_seven, npcs = 30, verbose = FALSE)
ElbowPlot(cells_seven, ndims = 30, reduction = "pca")
## recluster
cells_seven <- FindNeighbors(cells_seven, dims = 1:15)Computing nearest neighbor graph
Computing SNNcells_seven <- FindClusters(cells_seven, resolution = 1, random.seed = 42, algorithm = 2)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 192
Number of edges: 10432
Running Louvain algorithm with multilevel refinement...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.2651
Number of communities: 3
Elapsed time: 0 secondsDimPlot(cells_seven, reduction = "pca", dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters")
## plot cluster resolution = 1
## plot
DimPlot(cells_seven, reduction = "DIM_UMAP", dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") +
coord_fixed() +
theme_void() +
theme(legend.position = "bottom") +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
# PBANKA-0828000 GCSKO-3 GD1
# PBANKA-1302700 GCSKO-oom MD1
# PBANKA-1447900 GCSKO-29 MD2
# PBANKA-0102400 GCSKO-2 MD3
# PBANKA-0716500 GCSKO-19 MD4
# PBANKA-0413400 GCSKO-10_820 MD5
# PBANKA-1454800 GCSKO-21 FD1
# PBANKA-0902300 GCSKO-13 FD2
# PBANKA-1418100 GCSKO-17 FD3
# PBANKA-1435200 GCSKO-20 FD4
# PBANKA-1437500 - AP2G - commitment
## find markers
cells_seven_markers <- FindAllMarkers(cells_seven, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 5 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 13% ~01s
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|++++++++ | 15% ~01s
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|+++++++++++ | 22% ~01s
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|+++++++++++++++++++++++ | 46% ~01s
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|+++++++++++++++++++++++++ | 48% ~01s
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|++++++++++++++++++++++++++ | 51% ~01s
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|+++++++++++++++++++++++++++ | 53% ~00s
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|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~02s
|+ | 2 % ~02s
|++ | 3 % ~02s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|+++++++++ | 18% ~02s
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|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
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|++++++++++++++++++ | 35% ~01s
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|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
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|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
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markers_subset <- cells_seven_markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_logFC) %>% filter(p_val_adj < 0.05)
markers_subset <- markers_subset[order(-markers_subset$avg_logFC), ]
markers_subset
## find markers
markers_subset_mutant <- markers_subset[which(markers_subset$gene %in% list_of_mutant_genes), ]
markers_subset_mutant
## plot
#VlnPlot(cells_seven, features = c(markers_subset_mutant$gene, "PBANKA-1437500"), assay = "RNA")# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("slingshot")
library(slingshot)
library(RColorBrewer)
## extracts only 10x cells
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"),])
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X" & !tenx.mutant.integrated.sex@meta.data$seurat_clusters == "7"),])
## make a new Seurat of this
seurat.object <-subset(tenx.mutant.integrated.sex, cells = wt_cells)
## get UMAP coordinates
umap_coords <- seurat.object@reductions[["DIM_UMAP"]]@cell.embeddings
## old code: seurat.object@reductions$umapoptimised_post_repca@cell.embeddings
## new code: seurat.object@reductions[["DIM_UMAP"]]@cell.embeddings
## get clusters
#clusters <- as.list(seurat.object@meta.data$integrated_snn_res.4)
#names(clusters) <- rownames(seurat.object@meta.data)
#clusters <- as.list(clusters)
## cluster using kmeans
## you need to set a seed here to ensure the results are reproducible
set.seed(42)
clusters <- kmeans(umap_coords, centers = 13)$cluster
## plot
## make a nicer plot so we can interpret the clusters
df_plotting <- as.data.frame(cbind(umap_coords, clusters))
## change to character to make it discrete
df_plotting$clusters <- as.character(df_plotting$clusters)
## plot
ggplot(df_plotting, aes(x = DIMUMAP_1, y = DIMUMAP_2, colour = clusters)) +
geom_point() +
scale_colour_manual(values = rainbow(15)) +
theme_classic()
## initialise plot to prevent error
plot.new()
## slingshot to get lineages
lineage_uamp <- getLineages(umap_coords, clusters, start.clus = '5', end.clus = c('3', '16'))Using full covariance matrix## make a curve through lineage
crv1 <- getCurves(lineage_uamp)
## set up the colour pal
nb.cols <- 18
mycolors <- colorRampPalette(brewer.pal(9, "Set1"))(nb.cols)
## join points with line segments and plot
plot(umap_coords, col = mycolors[clusters], asp = 3, pch = 16)
lines(crv1, lwd = 3, col = 'black')
Visualise cells:
Add data to Seurat:
## extract data to add to Seurat
## extract clusters
meta_data_to_add_from_slingshot <- data.frame(clusters_k_means_UMAP = clusters)
## Add pseudotimes
# check the length of each branch to see which curve is which using: sum(is.na(as.data.frame(slingPseudotime(crv1))$curve1))
# then inspect using the ggplot2 above to where males are -
# tail(as.data.frame(slingPseudotime(crv1)), 100)
# tail(meta_data_to_add_from_slingshot, 100)
meta_data_to_add_from_slingshot$PT_Female_UMAP <- as.data.frame(slingPseudotime(crv1))$curve1
meta_data_to_add_from_slingshot$PT_Male_UMAP <- as.data.frame(slingPseudotime(crv1))$curve2
## add designation to SCE object
meta_data_to_add_from_slingshot$sex_UMAP <- "pre-det"
meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Female_UMAP))] <- "male"
meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Male_UMAP))] <- "female"
## if there are 3 curves in slingPseudotime(crv1):
#meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Female_UMAP) & is.na(as.data.frame(slingPseudotime(crv1))$curve3))] <- "male"
#meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Male_UMAP) & is.na(as.data.frame(slingPseudotime(crv1))$curve3))] <- "female"
## add clusters to SCE object
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, meta_data_to_add_from_slingshot)## plot
FeaturePlot(tenx.mutant.integrated.sex, label.size = 5, pt.size = 0.5, features = c("PT_Female_UMAP", "PT_Male_UMAP")) +
theme_void() +
theme(legend.position = "bottom") +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))plot sex designations
DimPlot(tenx.mutant.integrated.sex, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "sex_UMAP", na.value = "white") +
coord_fixed() +
theme_void() +
theme(legend.position = "bottom") +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))6. Monocle on sex cells
calculate pseudotime and modules
Preparation
monocle.object = learn_graph(monocle.object, learn_graph_control=list(ncenter=250, minimal_branch_len = 30), use_partition = FALSE)
|
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|============================================================================================================================| 100%# learn_graph_control=list(ncenter=500) - play with this parameter - lower tends to give fewer branches and higher tends to give more
# 500 - old analysis
# 250 - new analysis
## Plot cells
plot_cells(monocle.object, color_cells_by="partition", group_cells_by="partition", x = 2, y = 1)Cells aren't colored in a way that allows them to be grouped.
Define identities of cells
male
monocle.object_male <- choose_graph_segments(monocle.object)female
monocle.object_female <- choose_graph_segments(monocle.object)bipotential
monocle.object_bipot <- choose_graph_segments(monocle.object)asexual
monocle.object_asex <- choose_graph_segments(monocle.object)asexual fate
monocle.object_asex_fate <- choose_graph_segments(monocle.object)check
df_freq <- data.frame(table(c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))))
paste("number of cells in seurat object is", length(colnames(monocle.object)), ". The number of cells selected here with an identitity is", dim(df_freq)[1])[1] "number of cells in seurat object is 1022 . The number of cells selected here with an identitity is 1018"df_freq <- df_freq[df_freq$Freq > 1, ]
df_freqInspect where these missing cells are:
'%ni%' <- Negate('%in%')
not_assigned_cells <- colnames(monocle.object)[colnames(monocle.object) %ni% c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))]
DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = not_assigned_cells, dims = c(2,1), reduction = "DIM_UMAP") +
coord_fixed() +
scale_color_manual(values=c("#000000", "#f54e1e"))Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
## create annotation dataframe from these results:
df_monocle_sexes <- rbind(data.frame("cell_name" = colnames(monocle.object_male), "sex" = rep("Male", length(colnames(monocle.object_male)))),
data.frame("cell_name" = colnames(monocle.object_female), "sex" = rep("Female", length(colnames(monocle.object_female)))),
data.frame("cell_name" = colnames(monocle.object_bipot), "sex" = rep("Bipotential", length(colnames(monocle.object_bipot)))),
data.frame("cell_name" = colnames(monocle.object_asex), "sex" = rep("Asexual", length(colnames(monocle.object_asex)))),
data.frame("cell_name" = colnames(monocle.object_asex_fate), "sex" = rep("Asexual_Fate", length(colnames(monocle.object_asex_fate)))),
data.frame("cell_name" = not_assigned_cells, "sex" = rep("Unassigned", length(not_assigned_cells)))
)
dim(df_monocle_sexes)[1] 1022 2## order like the metadata
df_monocle_sexes <- df_monocle_sexes[match(rownames(monocle.object@colData), df_monocle_sexes$cell_name), ]
## add this back into the monocle object
monocle.object@colData$Sexes_monocle <- df_monocle_sexes$sexPseudotime Calculation
## Order the cells and calculate pseudotime
monocle.object = order_cells(monocle.object)
Listening on http://127.0.0.1:4574## Plot
umap_pt <- plot_cells(monocle.object, color_cells_by = "pseudotime", label_cell_groups=FALSE, cell_size = 1, x = 2, y = 1, label_branch_points=FALSE, label_leaves=FALSE, label_groups_by_cluster=FALSE, label_roots = FALSE) +
coord_fixed() +
theme_void() +
labs(title = "Pseudotime") +
theme(plot.title = element_text(hjust = 0.5))
## view
umap_pt
save
ggsave("../images_to_export/GCSKO_sexbranch_umap_pt.png", plot = umap_pt, device = "png", path = NULL, scale = 1, width = 10, height = 10, units = "cm", dpi = 300, limitsize = TRUE)Just check if the ‘hook’ on the males is a lineage or dead/dying/activated gams, or mutants
FeaturePlot(tenx.mutant.integrated.sex, features = "PBANKA-0416100", coord.fixed = TRUE, min.cutoff = "q1", dims = c(2,1), reduction = "DIM_UMAP", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("MG1 (Male)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
It appears MG1 marker expression is decreasing/off in these cells - inspect mutant status
plot <- DimPlot(tenx.mutant.integrated.sex, label = FALSE, label.size = 8, repel = FALSE, pt.size = 0.05, dims = c(2,1), reduction = "DIM_UMAP", group.by = "identity_updated") +
coord_fixed() +
theme(legend.position="bottom",
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank()) +
guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))
HoverLocator(plot = plot, information = FetchData(tenx.mutant.integrated.sex, vars = c("ident", "identity_updated", "nFeature_RNA")))the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used`error_y.color` does not currently support multiple values.`error_x.color` does not currently support multiple values.`line.color` does not currently support multiple values.The titlefont attribute is deprecated. Use title = list(font = ...) instead.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used`error_y.color` does not currently support multiple values.`error_x.color` does not currently support multiple values.`line.color` does not currently support multiple values.The titlefont attribute is deprecated. Use title = list(font = ...) instead.Check how well it correlates with the original pseudotime
when pseudotime was calculated on the whole object
library(ggpubr)
## extract pseudotime values:
pt_values_new <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
pt_values_new$cell_name <- rownames(pt_values_new)
meta_data_df <- as.data.frame(monocle.object@colData)
meta_data_df$cell_name <- rownames(meta_data_df)
meta_data_df <- merge(meta_data_df, pt_values_new, by = "cell_name")
names(meta_data_df)[ncol(meta_data_df)]<- "pt"
ggplot(meta_data_df, aes(x = old_pt_values, y = pt, colour = cluster_colours_figure)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
theme_classic() + stat_cor(method = "pearson")
This shows very good correlation.
Isolate Branches
```r
## access the closest principal graph node vertex for each cell and assign it as a column in your colData table using
#colData(monocle.object)$closest_vertex <- monocle.object@principal_graph_aux[[\UMAP\]]$pr_graph_cell_proj_closest_vertex[,1]
## plot
#plot_cells(monocle.object, color_cells_by = \closest_vertex\, label_cell_groups = FALSE)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Module Construction
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyMgZmluZCBnZW5lcyB0aGF0IGNoYW5nZSBhcyBhIGZ1bmN0aW9uIG9mIHB0OlxubW9ub2NsZS5vYmplY3RfcHJfdGVzdF9yZXMgPC0gZ3JhcGhfdGVzdChtb25vY2xlLm9iamVjdCwgbmVpZ2hib3JfZ3JhcGg9XCJwcmluY2lwYWxfZ3JhcGhcIiwgY29yZXM9OClcbmBgYCJ9 -->
```r
## find genes that change as a function of pt:
monocle.object_pr_test_res <- graph_test(monocle.object, neighbor_graph="principal_graph", cores=8)
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|=============================================================================================================| 100%, Elapsed 00:26## find significant genes
## I used 0.05 previously in all cells
## 2884 w. p = 0.01, 3260 w. p = 0.05
pr_deg_ids <- row.names(subset(monocle.object_pr_test_res, q_value < 0.05))
## collect into modules
gene_module_df_sex <- find_gene_modules(monocle.object[pr_deg_ids,], resolution=c(10^seq(-6,2)), random_seed = 1234)
## how many genes in modules?
dim(gene_module_df_sex)[1] 3695 5[1] "there are 20 modules"Plot Modules
General Module Composition
Make a dataframe to plot by aggregating clusters vs. modules
## make cell group df
cell_group_df <- tibble::tibble(cell=row.names(colData(monocle.object)), cell_group=colData(monocle.object)$seurat_clusters)
## make plotting df
agg_mat <- aggregate_gene_expression(monocle.object, gene_module_df_sex, cell_group_df)Find out how many genes there are per total so we can add this to the plot
## how many genes per module?
genes_per_module <- as.data.frame(table(gene_module_df_sex$module))
genes_per_moduleFind out which modules our mutant genes reside in
## create a list of our mutant gene IDs
list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")
## make a dataframe to convert the gene IDs into actual IDs
df_mutant_ids <- as.data.frame(unique(cbind(tenx.mutant.integrated@meta.data$identity_gene_updated, tenx.mutant.integrated@meta.data$identity_updated, tenx.mutant.integrated@meta.data$identity_name_updated)))[-c(1, 3),]
# remove the "820" bit on 10
df_mutant_ids$V1 <- gsub("_820", "", df_mutant_ids$V1)
# change the underscore (_) to a dash (-) in gene IDs
df_mutant_ids$V1 <- gsub("_", "-", df_mutant_ids$V1)
names(df_mutant_ids) <- c("gene_ID", "mutant_identity", "mutant_identity_2")
## subset modules df to include only mutant gene IDs
df_mutant_gene_modules <- as.data.frame(gene_module_df_sex[which(gene_module_df_sex$id %in% list_of_mutant_genes),])
names(df_mutant_gene_modules)[1] <- "gene_ID"
## merge dataframes
df_mutant_gene_modules <- merge(df_mutant_gene_modules, df_mutant_ids, by = "gene_ID")
## Inspect
df_mutant_gene_modulesso modules of interest are:
table(df_mutant_gene_modules$module)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0 0 0 0 0 1 0 0 0 6 0 0 0 0 0 0 1 0 0 2 Which modules do other genes of interest lie in?:
## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300
# ccp2 - "PBANKA-1319500" - female 820
# p25 - "PBANKA-0515000" - female
# p28 - "PBANKA-0514900" - female
# ccp3 - "PBANKA-1035200" - female - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909122/
# nek4 - "PBANKA-0616700" - female
# ap2-fg - "PBANKA-1415700" - female
# dozi - "PBANKA-1217700" - female
# MG1 - "PBANKA-0416100" - male 820
# hap2 - "PBANKA-1212600" - male
# MAPK2 - "PBANKA-0933700" - male
# nek1 - "PBANKA-1443000" - male
# cdpk4 - "PBANKA-0615200" - male
## create a list of landmark genes
list_of_landmark_genes <- c("PBANKA-1437500",
"PBANKA-0909600",
"PBANKA-1034300",
"PBANKA-1319500",
"PBANKA-0515000",
"PBANKA-0514900",
"PBANKA-1035200",
"PBANKA-0616700",
"PBANKA-1415700",
"PBANKA-1217700",
"PBANKA-0416100",
"PBANKA-1212600",
"PBANKA-0933700",
"PBANKA-1443000",
"PBANKA-0615200")
name_of_landmark_genes <- c("AP2-G",
"AP2_poran",
"AP2-G2",
"ccp2",
"p25",
"p28",
"ccp3",
"nek4",
"ap2-fg",
"DOZI",
"mg1",
"hap2",
"mapk2",
"nek1",
"cdpk4")
## make a df
name_of_landmark_genes <- data.frame("gene_name" = name_of_landmark_genes, "id" = list_of_landmark_genes)
## make dataframe
df_landmark_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% list_of_landmark_genes),]
## merge dataframes
df_landmark_gene_modules <- merge(df_landmark_gene_modules, name_of_landmark_genes, by = "id")
## inspect
df_landmark_gene_modulesenrichment of screen hits in modules
## read in screen hits
library("readxl")
screen_hits <- read_excel("../data/Screen/Modules_Clusters_Phenotypes.xlsx")
## get only hits
screen_hits_selected <- screen_hits[which(screen_hits$`Gam phenotype screen` == "Both" | screen_hits$`Gam phenotype screen` == "Females" | screen_hits$`Gam phenotype screen` == "Males"), ]
## extract gene IDs
gene_hits <- screen_hits_selected$`new gene ID`
## change the underscore to a dash
gene_hits <- gsub("_", "-", gene_hits)
## find out which modules they are in
df_hits_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% gene_hits),]
print("screen hits by module")[1] "screen hits by module"table(df_hits_gene_modules$module)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
7 1 0 1 0 2 0 0 1 1 3 2 8 0 0 3 1 0 1 ## get the number genes screened in that module
screen_hits_screened <- screen_hits[which(screen_hits$`Gam phenotype screen` == "Both" | screen_hits$`Gam phenotype screen` == "Females" | screen_hits$`Gam phenotype screen` == "Males" | screen_hits$`Gam phenotype screen` == "None" | screen_hits$`Gam phenotype screen` == "male not enough power"), ]
## extract gene IDs
genes_screened <- screen_hits_screened$`new gene ID`
## change the underscore to a dash
genes_screened <- gsub("_", "-", genes_screened)
## find out which modules they are in
df_screened_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% genes_screened),]
print("total genes screened in this module")[1] "total genes screened in this module"table(df_screened_gene_modules$module)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
62 42 87 38 42 17 46 90 31 31 38 43 57 49 45 18 33 40 20 ## make a table with this info
pc_screened <- data.frame(hits = table(df_hits_gene_modules$module), screened = table(df_screened_gene_modules$module))[,-3]
names(pc_screened) <- c("module", "hits", "screened")
pc_screened$pc <- (pc_screened$hits /pc_screened$screened)*100
## view
pc_screenedFurther investigation of screen hits
## rename df
df_screen_hits_selected <- as.data.frame(screen_hits_selected)
df_screen_hits_selected$'new gene ID' <- gsub("_", "-", df_screen_hits_selected$'new gene ID')
names(df_screen_hits_selected)[2] <- "id"
df_gene_module_df_sex <- as.data.frame(gene_module_df_sex)
## merge dfs together
screen_hits_modules <- merge(df_screen_hits_selected, df_gene_module_df_sex, by = "id")
## view
screen_hits_modulesDOZI-regulated genes
Find out how many of the genes in each module has a DOZI-regulated gene
DOZI_regulated_genes <-
read.csv(file = "../data/Reference/DOZI_regulated_genes.csv", header = TRUE)
## extract gene IDs
dozi_genes <- DOZI_regulated_genes[DOZI_regulated_genes$DOZI_regulated. == "YES", ]$Gene_ID_PB
## change the underscore to a dash
dozi_genes <- gsub("_", "-", dozi_genes)
## find out which modules they are in
df_dozi_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% dozi_genes),]
print("dozi genes by module")[1] "dozi genes by module"table(df_dozi_gene_modules$module)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
16 2 4 10 11 2 8 72 72 3 6 5 13 10 6 4 5 6 2 3 Visualise module expression
plot out modules
## make aggregated df again so you can edit it
agg_mat <- aggregate_gene_expression(monocle.object, gene_module_df_sex, cell_group_df)
## h clust the aggregated matrix
module_dendro <- hclust(dist(agg_mat))
## use these clusters to reorder the modules
gene_module_df_sex$module <- factor(gene_module_df_sex$module, levels = row.names(agg_mat)[module_dendro$order])
## plot
UMAP_modules <- plot_cells(monocle.object, genes=gene_module_df_sex %>% filter(module %in% c(1:20)),
cell_size = 2,
x = 2, y = 1,
label_cell_groups=FALSE,
scale_to_range = TRUE,
show_trajectory_graph=FALSE) +
scale_colour_viridis_c(name = "expression", option = "C", alpha = 1) +
coord_fixed() +
theme_void() +
theme(legend.position = "bottom", strip.text.x = element_text(size = 15))Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.## view
UMAP_modules
save
ggsave("../images_to_export/GCSKO_sexbranch_umap_modules.png", plot = UMAP_modules, device = "png", path = NULL, scale = 1, width = 30, height = 30, units = "cm", dpi = 300, limitsize = TRUE)## make a dataframe of genes per module
genes_per_module <- as.data.frame(table(gene_module_df_sex$module))
## inspect
genes_per_module## A. Preparation of dataframe
## prepare custom dataframe for all cells by modules:
agg_mat_no_group <- aggregate_gene_expression(monocle.object, gene_module_df_sex, scale_agg_values = TRUE)
## B. annotations
## make an anotation
## add pt to the monocle object
monocle.object@colData$pt <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
## make an annotation dataframe
anno_no_group <- data.frame(monocle.object@colData$cluster_colours_figure, monocle.object@colData$pt, as.factor(monocle.object@colData$Prediction.Spearman._Kasia), row.names = rownames(monocle.object@colData))
names(anno_no_group) <- c("Sex", "Pseudotime", "Real_time_prediction")
## make annotation colours
annotation_colours <- list(Sex = c(Male="#016c00", Female="#a52b1e", Bipotential = "#ffe400", Asexual = "#0052c5"),
Pseudotime = plasma(12, direction = 1),
Real_time_prediction = c("0" = viridis(8, direction = 1)[1] ,"1" = viridis(8, direction = 1)[2] ,"4" = viridis(8, direction = 1)[3] ,"6" = viridis(8, direction = 1)[4] ,"8" = viridis(8, direction = 1)[5] ,"12" = viridis(8, direction = 1)[6] ,"18" = viridis(8, direction = 1)[7] ,"24" = viridis(8, direction = 1)[8]))
## change the order of the cols (cells) in data frame
col.order <- rownames(anno_no_group[with(anno_no_group, order(Sex, Pseudotime)), ])
agg_mat_no_group <- agg_mat_no_group[,col.order]
## reorder the rows (gene modules) in the data frame so they are in pt order
## define the order visually and using the clusters originally produced
row.order <- c("8", "3","4", "15", "2", "18", "11",
"7", "1", "13",
"10", "14", "12", "16", "9",
"5","6", "17",
"19", "20")
## reorder using new order
agg_mat_no_group <- agg_mat_no_group[row.order, ]
## for cuts in columns - used later to count number of cells in each cat
df_meta <- as.data.frame(monocle.object@colData)
female_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Female"), ])
male_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Male"), ])
bi_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Bipotential"), ])
asex_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Asexual"), ])
rm(df_meta)
## add module and the number of cells to the row
## change names for row names to include "module " at the begining of them
labels.row <- stringr::str_c("Module ", row.names(agg_mat_no_group))
## reorder frequency so that it is matching our matrix
genes_per_module_ordered <- genes_per_module[match(row.names(agg_mat_no_group), genes_per_module$Var1), ]
## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module_ordered$Freq)){
labels.row[i] <- stringr::str_c(labels.row[i]," (n = " ,genes_per_module_ordered$Freq[i], ")")
}
## C. Plotting
## plot
heatmap_main <- pheatmap::pheatmap(agg_mat_no_group,
#scale="row",
cluster_cols = FALSE,
cluster_rows = FALSE,
## others: ward.D2,
#clustering_method="complete",
show_colnames = FALSE,
labels_row = labels.row,
fontsize_row = 15,
fontsize = 15,
gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
annotation_col = anno_no_group,
annotation_colors = annotation_colours,
cutree_rows = 6)
## view
heatmap_main
save
ggsave("../images_to_export/GCSKO_sexbranch_heatmap.png", plot = heatmap_main, device = "png", path = NULL, scale = 1, width = 27, height = 20, units = "cm", dpi = 300, limitsize = TRUE)
This is for:
## repeat of plot above:
## Plot
umap_pt <- plot_cells(monocle.object, color_cells_by = "pseudotime", label_cell_groups=FALSE, cell_size = 1, x = 2, y = 1, label_branch_points=FALSE, label_leaves=FALSE, label_groups_by_cluster=FALSE, label_roots = FALSE) +
coord_fixed() +
theme_void() +
labs(title = "Pseudotime") +
theme(plot.title = element_text(hjust = 0.5))
## view
umap_pt
Visualise the expression of select genes over pseudotime
## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300
#list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")
## define genes to plot
list_of_genes_of_interest <- c("PBANKA-0102400", "PBANKA-0413400", "PBANKA-0716500", "PBANKA-0828000", "PBANKA-0902300", "PBANKA-1144800", "PBANKA-1302700", "PBANKA-1418100", "PBANKA-1435200", "PBANKA-1447900", "PBANKA-1454800", "PBANKA-1437500", "PBANKA-1319500", "PBANKA-0416100", "PBANKA-1034300")
## add names
names_of_genes_of_interest <- c("GCSKO-2", "GCSKO-10", "GCSKO-19", "GCSKO-3", "GCSKO-13", "GCSKO-28", "GCSKO-oom", "GCSKO-17", "GCSKO-20", "GCSKO-29", "GCSKO-21", "AP2-G", "CCP2", "MG1", "AP2-G2")
##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)), name = names_of_genes_of_interest)
## reorder
#genes_of_interest <- genes_of_interest[match(c("AP2-G", "CCP2", "GCSKO-21", "GCSKO-17", "GCSKO-2", "MG1", "GCSKO-20", "GCSKO-3", "GCSKO-oom", "GCSKO-29", "GCSKO-10", "GCSKO-28", "GCSKO-19", "GCSKO-13"), genes_of_interest$name), ]
## prepare custom dataframe for all cells by modules:
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest[,1:2])
## reorder using new order
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,col.order]
## plot
heatmap_plot <- pheatmap::pheatmap(agg_mat_genes_of_interest,
#scale="row",
cluster_cols = FALSE,
cluster_rows = TRUE,
clustering_method="ward.D2",
show_colnames = FALSE,
labels_row = as.character(genes_of_interest[,3]),
gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
#gaps_row = c(1, 6),
cutree_rows = 2,
## trying to fix legend issue here
#fontsize_row = 10,
#fontsize_col = 3,
#cellheight=3,
#cellwidth = 3,
legend = TRUE,
annotation_legend = FALSE,
annotation_col = anno_no_group,
annotation_colors = annotation_colours
)
heatmap_plot
look at some of the “module” genes now too
## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300
#list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")
## define genes to plot
list_of_genes_of_interest <- c("PBANKA-1454000", "PBANKA-1354000")
## add names
names_of_genes_of_interest <- c("GyrB", "DBR1")
##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)), name = names_of_genes_of_interest)
## reorder
#genes_of_interest <- genes_of_interest[match(c("AP2-G", "CCP2", "GCSKO-21", "GCSKO-17", "GCSKO-2", "MG1", "GCSKO-20", "GCSKO-3", "GCSKO-oom", "GCSKO-29", "GCSKO-10", "GCSKO-28", "GCSKO-19", "GCSKO-13"), genes_of_interest$name), ]
## prepare custom dataframe for all cells by modules:
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest[,1:2])
## reorder using new order
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,col.order]
## plot
heatmap_plot <- pheatmap::pheatmap(agg_mat_genes_of_interest,
#scale="row",
cluster_cols = FALSE,
cluster_rows = TRUE,
clustering_method="ward.D2",
show_colnames = FALSE,
labels_row = as.character(genes_of_interest[,3]),
gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
#gaps_row = c(1, 6),
cutree_rows = 2,
## trying to fix legend issue here
#fontsize_row = 10,
#fontsize_col = 3,
#cellheight=3,
#cellwidth = 3,
legend = TRUE,
annotation_legend = FALSE,
annotation_col = anno_no_group,
annotation_colors = annotation_colours
)
heatmap_plot
Plot specific genes of interest
Side plots
construct new dataframes for the cells from mutants for each sex
```r
## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly
## male
## subset out only male and pre determination cells
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$at_sex == \male\), ]
## take forward only smart-seq2
male_cells <- male_cells[which(male_cells$experiment == \mutants\), ]
## get cell names
male_cells <- rownames(male_cells)
## subset Seurat object to contain cells of interest
male.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = male_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(male.seurat.object, assay = \RNA\, slot = \data\)), 'sparseMatrix')
pd <- data.frame(male.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
male.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
male.monocle.object = preprocess_cds(male.monocle.object, num_dim = 50, norm_method = \none\)
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds
#male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, exclude.na = FALSE)
## female
## subset out only male and pre determination cells
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$at_sex == \female\), ]
## take forward only wild-type
female_cells <- female_cells[which(female_cells$experiment == \mutants\), ]
## get cell names
female_cells <- rownames(female_cells)
## subset our cell group df to keep only these cells
#female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## subset Seurat object to contain cells of interest
female.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = female_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(female.seurat.object, assay = \RNA\, slot = \data\)), 'sparseMatrix')
pd <- data.frame(female.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
female.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
female.monocle.object = preprocess_cds(female.monocle.object, num_dim = 50, norm_method = \none\)
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds
#female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, exclude.na = FALSE)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
male
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
# male_agg_mat
## reorder using new order
male_agg_mat <- male_agg_mat[row.order, ]
## make an anotation
anno_male <- data.frame(male.monocle.object@colData$at_sex, male.monocle.object@colData$old_pt_values, genotype = male.monocle.object@colData$identity_updated, row.names = rownames(male.monocle.object@colData))
names(anno_male) <- c(\sex\, \Pseudotime\, \genotype\)
## make annotation colours
annotation_colours <- list(sex = c(male=\#016c00\, female=\#a52b1e\, 'pre-det' = \#0052c5\),
Pseudotime = magma(12, direction = 1))
## change the order of the data frame
col.order.male <- rownames(anno_male[with(anno_male, order(genotype, Pseudotime)), ])
male_agg_mat <- male_agg_mat[,col.order.male]
## plot
heatmap_male <- pheatmap::pheatmap(male_agg_mat,
#scale=\row\,
cluster_cols = FALSE,
cluster_rows = FALSE,
clustering_method=\ward.D2\,
show_colnames = FALSE,
legend = FALSE,
annotation_legend = TRUE,
annotation_col = anno_male,
annotation_colors = annotation_colours,
cutree_rows = 12)
heatmap_male
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
# female_agg_mat
## reorder using new order
female_agg_mat <- female_agg_mat[row.order, ]
## make an anotation
anno_female <- data.frame(female.monocle.object@colData$at_sex, female.monocle.object@colData$old_pt_values, genotype = female.monocle.object@colData$identity_updated, row.names = rownames(female.monocle.object@colData))
names(anno_female) <- c(\sex\, \Pseudotime\, \genotype\)
## make annotation colours
annotation_colours <- list(sex = c(male=\#016c00\, female=\#a52b1e\, 'pre-det' = \#0052c5\),
Pseudotime = magma(12, direction = 1))
## change the order of the data frame
col.order.female <- rownames(anno_female[with(anno_female, order(genotype, Pseudotime)), ])
female_agg_mat <- female_agg_mat[,col.order.female]
## plot
heatmap_female <- pheatmap::pheatmap(female_agg_mat,
#scale=\row\,
cluster_cols = FALSE,
cluster_rows = FALSE,
clustering_method=\ward.D2\,
show_colnames = FALSE,
legend = FALSE,
annotation_legend = FALSE,
annotation_col = anno_female,
annotation_colors = annotation_colours,
cutree_rows = 12)
heatmap_female
## save a pheatmap: https://stackoverflow.com/questions/43051525/how-to-draw-pheatmap-plot-to-screen-and-also-save-to-file
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
side plots with groups of mutant cells
female
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
## make a new grouping for cells based on their identity
mutant_group_female <- data.frame(cell = rownames(female.monocle.object@colData), cell_group = female.monocle.object@colData$identity_updated)
## aggregate expression
female_agg_mat_grouped <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, mutant_group_female, exclude.na = FALSE)
## reorder using new order
female_agg_mat_grouped <- female_agg_mat_grouped[row.order, ]
## plot
pheatmap::pheatmap(female_agg_mat_grouped,
#scale=\row\,
cluster_cols = TRUE,
cluster_rows = FALSE,
clustering_method=\ward.D2\,
show_colnames = TRUE,
legend = FALSE,
annotation_legend = FALSE,
#annotation_col = anno_female,
#annotation_colors = annotation_colours,
cutree_rows = 12)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
male
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
module 12 inspection
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
## make a df for module 12 genes
module.12.genes <- gene_module_df_sex[gene_module_df_sex$module == 12, ]$id
module.12.genes <- data.frame(id = module.12.genes, group = module.12.genes)
## prepare custom dataframe for all cells by modules:
agg_mat_module_12 <- aggregate_gene_expression(monocle.object, module.12.genes)
## make an anotation
anno_no_group <- data.frame(monocle.object@colData$sex_UMAP, monocle.object@colData$old_pt_values, row.names = rownames(monocle.object@colData))
names(anno_no_group) <- c(\sex\, \Pseudotime\)
## make annotation colours
annotation_colours <- list(sex = c(male=\#016c00\, female=\#a52b1e\, 'pre-det' = \#0052c5\),
Pseudotime = magma(12, direction = 1))
## change the order of the data frame
col.order <- c(pre_det_ordered_cells, female_ordered_cells, male_ordered_cells)
agg_mat_module_12 <- agg_mat_module_12[,col.order]
## plot
pheatmap::pheatmap(agg_mat_module_12,
#scale=\row\,
cluster_cols = FALSE,
clustering_method=\ward.D2\,
show_colnames = FALSE,
annotation_col = anno_no_group,
annotation_colors = annotation_colours,
fontsize_row = 7,
cutree_rows = 12)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
save plot
<!-- rnb-text-end -->
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```r
```r
heatmap_module_12 <- pheatmap::pheatmap(agg_mat_module_12,
#scale=\row\,
cluster_cols = FALSE,
clustering_method=\ward.D2\,
show_colnames = FALSE,
annotation_col = anno_no_group,
annotation_colors = annotation_colours,
fontsize_row = 7,
cutree_rows = 12)
<!-- rnb-source-end -->
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AP2 Expression
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```r
```r
## reading table of AP2 genes
ap2_genes_table <- read.delim(file = \~/data/AP2_genes_table.txt\, header = TRUE, sep =\\t\)
## extract list of genes
ap2_genes_list <- dplyr::pull(ap2_genes_table, Gene.ID)
ap2_genes_list <- gsub(\_\, \-\, ap2_genes_list)
## make a df for genes
ap2_genes_list <- data.frame(id = ap2_genes_list, group = ap2_genes_list)
## prepare custom dataframe for all cells by modules:
agg_mat_ap2s <- aggregate_gene_expression(monocle.object, ap2_genes_list)
## change the order of the data frame
col.order <- c(pre_det_ordered_cells, female_ordered_cells, male_ordered_cells)
agg_mat_ap2s <- agg_mat_ap2s[,col.order]
## plot
pheatmap::pheatmap(agg_mat_ap2s,
#scale=\row\,
cluster_cols = FALSE,
clustering_method=\complete\,
show_colnames = FALSE,
annotation_col = anno_no_group,
annotation_colors = annotation_colours,
fontsize_row = 7,
cutree_rows = 3)
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### Module Analysis
Read in Kasia's modules:
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```r
```r
## read in kasia modules:
kasia_clusters <- read.csv(file = \~/data/Modules_Clusters_Phenotypes.csv\, header = TRUE)
## change _ to -:
kasia_clusters$new.gene.ID <- gsub(\_\, \-\, kasia_clusters$new.gene.ID)
## filter out genes not in modules gene_module_df_sex:
kasia_clusters_filtered <- kasia_clusters[which(kasia_clusters$new.gene.ID %in% gene_module_df_sex$id), ]
## rename new gene id
names(kasia_clusters_filtered)[2] <- \id\
## merge together
modules_merged_df <- merge(kasia_clusters_filtered, gene_module_df_sex, by = \id\)
## look at the enrichment with a dotplot:
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(modules_merged_df$Kasia.Cluster, df_meta_data$identity_combined), margin = 2)) * 100)
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NOT USED
complex heatmap version
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```r
```r
## make into matrix to plot
col.order <- agg_mat_all_cells_matrix <- as.matrix(agg_mat_all_cells)
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make annotation
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```r
```r
## extract pseudotime values:
pt_values <- as.data.frame(pseudotime(monocle.object, reduction_method = \UMAP\))
names(pt_values) <- \monocle_pt_sex_wt\
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, pt_values)
## save pt values
write.csv(pt_values, file = \~/data_to_export/pt_values_sex_only.csv\)
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```r
```r
library(circlize)
## make the annotation df
## get meta data from seurat object and then subset rows out that are wt
df_anno <- tenx.mutant.integrated.sex@meta.data[which(rownames(tenx.mutant.integrated.sex@meta.data) %in% colnames(agg_mat_all_cells_matrix)), ]
## get only columns of interest:
df_anno <- df_anno[ ,c(\sex\, \monocle_pt_sex_wt\), drop = FALSE ]
## order annotation
df_anno <- df_anno[with(df_anno, order(sex, monocle_pt_sex_wt)),]
## order cols in the matrix
agg_mat_all_cells_matrix <- agg_mat_all_cells_matrix[ ,match(colnames(agg_mat_all_cells_matrix), rownames(df_anno))]
## make annotation
heatmap_annotation <- HeatmapAnnotation(df = cluster_anno,
col = list(sex = c(male=\#016c00\, female=\#a52b1e\, `pre-det` = \#0052c5\), monocle_pt_sex_wt = colorRamp2(c(1:70), inferno(70)))
)
heatmap_annotation <- HeatmapAnnotation(sex = df_anno$sex, pt = df_anno$monocle_pt_sex_wt)
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plot
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```r
```r
## make heatmap
modules_heatmap <- Heatmap(agg_mat_all_cells_matrix,
column_order = NULL,
cluster_columns = FALSE,
cluster_rows = FALSE,
show_column_dend = FALSE,
column_labels = rep(\\, ncol(agg_mat_all_cells_matrix)),
#row_order = module_dendro$order,
clustering_method_columns = \ward.D2\,
bottom_annotation = heatmap_annotation,
col = colorRampPalette(rev(brewer.pal(n = 7, name =
\RdYlBu\)))(100),
heatmap_legend_param = list(direction = \horizontal\))
## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = \bottom\,
annotation_legend_side = \bottom\)
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```r
```r
## extract counts from 10x object
matrix_tenx_counts <- as.matrix(GetAssayData(pb_sex_filtered, assay = \RNA\))
#nk.raw.data <- as.matrix(GetAssayData(pb_sex_filtered, slot = \counts\)[, WhichCells(pbmc, ident = \NK\)])
## check it is the same as the merged object RNA slot
## check it is the same as the monocle object
matrix_tenx_counts_monocle <- as.matrix(as.data.frame((monocle.object@assays)))
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```r
```r
## make heatmap
modules_heatmap <- Heatmap(matrix_tenx_counts,
column_order = NULL,
cluster_columns = TRUE,
cluster_rows = FALSE,
show_column_dend = FALSE,
column_labels = rep(\\, ncol(matrix_tenx_counts)),
#row_order = module_dendro$order,
clustering_method_columns = \ward.D2\,
#bottom_annotation = heatmap_annotation,
col = colorRampPalette(rev(brewer.pal(n = 7, name =
\RdYlBu\)))(100),
heatmap_legend_param = list(direction = \horizontal\))
## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = \bottom\,
annotation_legend_side = \bottom\)
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#### plot as a function of pseudotime
Now, we will integrate the branch data we produced using slingshot and the pseudotime values to plot this heatmap.
Monocle3 has a handy function that allows us to aggregate expression of groups of cells called aggregate_gene_expression.
The code for this is located here: https://github.com/cole-trapnell-lab/monocle3/blob/1a02274209c765fe7a60f533a31b1da3dacf6785/R/cluster_genes.R
Define the groups of cells
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```r
```r
## Split cells into groups of sexes
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \female\), ]
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \male\), ]
pre_det_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \pre-det\), ]
## inspect range of pt values to determine bin width
hist(female_cells$PT_LineageFemale)
hist(male_cells$PT_LineageMale)
hist(pre_det_cells$PT_LineageFemale)
hist(pre_det_cells$PT_LineageMale)
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Use a bin width of 2
there will be two objects for the cell_group_df: male branch and female branch. Both will include the pre-det branch
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```r
```r
## Define male and female branch cells
# male
male_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \pre-det\ | tenx.mutant.integrated.sex@meta.data$sex == \male\), ]
male_branch_meta_data <- data.frame(cell_id = rownames(male_branch_meta_data), pt = male_branch_meta_data$PT_LineageMale)
male_cell_group_df <- male_branch_meta_data
#female
female_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \pre-det\ | tenx.mutant.integrated.sex@meta.data$sex == \female\), ]
female_branch_meta_data <- data.frame(cell_id = rownames(female_branch_meta_data), pt = female_branch_meta_data$PT_LineageFemale)
female_cell_group_df <- female_branch_meta_data
## what's the range of values for each pt?
range(female_cell_group_df$pt)
range(male_cell_group_df$pt)
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```r
```r
## make bin widths
# make a new col for annotation
female_cell_group_df$pt_bin <- NA
for(i in seq(2,68,2)){
female_cell_group_df$pt_bin[which(female_cell_group_df$pt < i & female_cell_group_df$pt >= (i-2))] <- i
}
male_cell_group_df$pt_bin <- NA
for(i in seq(2,68,2)){
male_cell_group_df$pt_bin[which(male_cell_group_df$pt < i & male_cell_group_df$pt >= (i-2))] <- i
}
# then remove old pt values
male_cell_group_df <- male_cell_group_df[ ,-2]
female_cell_group_df <- female_cell_group_df[ ,-2]
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-->
```r
```r
## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly
## male
## subset out only male and pre determination cells
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \male\ | tenx.mutant.integrated.sex@meta.data$sex == \pre-det\), ]
## take forward only wild-type
male_cells <- male_cells[which(male_cells$identity_combined == \WT\ | male_cells$identity_combined == \WT_10X\), ]
#male_cells <- male_cells[which(male_cells$identity_combined == \WT_10X\), ]
## get cell names
male_cells <- rownames(male_cells)
## subset our cell group df to keep only these cells
male_cell_group_df <- male_cell_group_df[which(male_cell_group_df$cell_id %in% male_cells),]
## subset Seurat object to contain cells of interest
male.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = male_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(male.seurat.object, assay = \RNA\, slot = \data\)), 'sparseMatrix')
pd <- data.frame(male.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
male.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
male.monocle.object = preprocess_cds(male.monocle.object, num_dim = 50, norm_method = \none\)
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds
male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, male_cell_group_df, exclude.na = FALSE)
## female
## subset out only male and pre determination cells
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \female\ | tenx.mutant.integrated.sex@meta.data$sex == \pre-det\), ]
## take forward only wild-type
female_cells <- female_cells[which(female_cells$identity_combined == \WT\ | female_cells$identity_combined == \WT_10X\), ]
#female_cells <- female_cells[which(female_cells$identity_combined == \WT_10X\), ]
## get cell names
female_cells <- rownames(female_cells)
## subset our cell group df to keep only these cells
female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## subset Seurat object to contain cells of interest
female.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = female_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(female.seurat.object, assay = \RNA\, slot = \data\)), 'sparseMatrix')
pd <- data.frame(female.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
female.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
female.monocle.object = preprocess_cds(female.monocle.object, num_dim = 50, norm_method = \none\)
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds
female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, female_cell_group_df, exclude.na = FALSE)
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<!-- rnb-source-begin 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 -->
```r
```r
## use these clusters to reorder the modules
male_agg_mat <- male_agg_mat[match(levels(gene_module_df_sex$module), row.names(male_agg_mat)), ]
female_agg_mat <- female_agg_mat[match(levels(gene_module_df_sex$module), row.names(female_agg_mat)), ]
pheatmap::pheatmap(male_agg_mat,
scale=\row\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE)
pheatmap::pheatmap(male_agg_mat,
scale=\column\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE)
pheatmap::pheatmap(male_agg_mat,
scale=\none\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE)
pheatmap::pheatmap(female_agg_mat,
scale=\row\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE)
pheatmap::pheatmap(female_agg_mat,
scale=\column\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE)
pheatmap::pheatmap(female_agg_mat,
scale=\none\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE)
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ComplexHeatmap version
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<!-- rnb-source-begin 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 -->
```r
```r
## pheatmap calculates Z scores for plotting values
scale_matrix_by_cols <- function (x)
{
m = apply(x, 1, mean, na.rm = T)
s = apply(x, 1, sd, na.rm = T)
return((x - m)/s)
}
## calculate z score by col
female_agg_mat_scaled <- t(as.matrix(scale_matrix_by_cols(t(female_agg_mat))))
male_agg_mat_scaled <- t(as.matrix(scale_matrix_by_cols(t(male_agg_mat))))
## reorder cols
female_agg_mat_scaled <- female_agg_mat_scaled[match(levels(gene_module_df_sex$module), row.names(female_agg_mat_scaled)), ]
male_agg_mat_scaled <- male_agg_mat_scaled[match(levels(gene_module_df_sex$module), row.names(male_agg_mat_scaled)), ]
## reorder based on clusters
genes_per_module <- genes_per_module[match(levels(gene_module_df_sex$module), row.names(genes_per_module)), ]
## change names for row names to include \module \ at the begining of them
row.names(female_agg_mat_scaled) <- stringr::str_c(\Module \, row.names(female_agg_mat_scaled))
row.names(male_agg_mat_scaled) <- stringr::str_c(\Module \, row.names(male_agg_mat_scaled))
## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module$Freq)){
row.names(female_agg_mat_scaled)[i] <- stringr::str_c(row.names(female_agg_mat_scaled)[i],\ (n = \ ,genes_per_module$Freq[i], \)\)
}
for(i in seq_along(genes_per_module$Freq)){
row.names(male_agg_mat_scaled)[i] <- stringr::str_c(row.names(male_agg_mat_scaled)[i],\ (n = \ ,genes_per_module$Freq[i], \)\)
}
## add annotation:
#heatmap_annotation <- HeatmapAnnotation(df = cluster_anno,
# col = list(
# Identity = c(Male=\#016c00\, Female=\#a52b1e\, Asexual= \#0052c5\, Committed = \#f2eb23\)),
# annotation_legend_param = list(Median_Pseudotime_of_Cluster = list(direction = \horizontal\), Identity = list(nrow = 1)))
library(ComplexHeatmap)
library(RColorBrewer)
modules_heatmap_female <- Heatmap(female_agg_mat_scaled,
column_order = NULL,
#row_order = row.names(female_agg_mat_scaled)[module_dendro$order],
#clustering_method_rows = \ward.D2\,
#bottom_annotation = heatmap_annotation,
col = colorRampPalette(rev(brewer.pal(n = 7, name =
\RdYlBu\)))(100),
heatmap_legend_param = list(direction = \horizontal\))
modules_heatmap_male <- Heatmap(male_agg_mat_scaled,
column_order = NULL,
#row_order = module_dendro$order,
#clustering_method_rows = \ward.D2\,
#bottom_annotation = heatmap_annotation,
col = colorRampPalette(rev(brewer.pal(n = 7, name =
\RdYlBu\)))(100),
heatmap_legend_param = list(direction = \horizontal\))
draw(modules_heatmap_female, merge_legend = TRUE, heatmap_legend_side = \bottom\,
annotation_legend_side = \bottom\)
draw(modules_heatmap_male, merge_legend = TRUE, heatmap_legend_side = \bottom\,
annotation_legend_side = \bottom\)
## https://www.biostars.org/p/380544/
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4 bin width
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<!-- rnb-source-begin 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 -->
```r
```r
## Define male and female branch cells
# male
male_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \pre-det\ | tenx.mutant.integrated.sex@meta.data$sex == \male\), ]
male_branch_meta_data <- data.frame(cell_id = rownames(male_branch_meta_data), pt = male_branch_meta_data$PT_LineageMale)
male_cell_group_df <- male_branch_meta_data
#female
female_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \pre-det\ | tenx.mutant.integrated.sex@meta.data$sex == \female\), ]
female_branch_meta_data <- data.frame(cell_id = rownames(female_branch_meta_data), pt = female_branch_meta_data$PT_LineageFemale)
female_cell_group_df <- female_branch_meta_data
## what's the range of values for each pt?
range(female_cell_group_df$pt)
range(male_cell_group_df$pt)
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<!-- rnb-source-begin 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 -->
```r
```r
## make bin widths
# make a new col for annotation
female_cell_group_df$pt_bin <- NA
for(i in seq(4,68,4)){
female_cell_group_df$pt_bin[which(female_cell_group_df$pt < i & female_cell_group_df$pt >= (i-4))] <- i
}
male_cell_group_df$pt_bin <- NA
for(i in seq(4,68,4)){
male_cell_group_df$pt_bin[which(male_cell_group_df$pt < i & male_cell_group_df$pt >= (i-4))] <- i
}
# then remove old pt values
male_cell_group_df <- male_cell_group_df[ ,-2]
female_cell_group_df <- female_cell_group_df[ ,-2]
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```r
```r
## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly
## male
## subset our cell group df to keep only these cells
male_cell_group_df <- male_cell_group_df[which(male_cell_group_df$cell_id %in% male_cells),]
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds
male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, male_cell_group_df, exclude.na = FALSE)
## female
## subset out only male and pre determination cells
## subset our cell group df to keep only these cells
female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds
female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, female_cell_group_df, exclude.na = FALSE)
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```r
```r
## use these clusters to reorder the modules
male_agg_mat <- male_agg_mat[match(levels(gene_module_df_sex$module), row.names(male_agg_mat)), ]
female_agg_mat <- female_agg_mat[match(levels(gene_module_df_sex$module), row.names(female_agg_mat)), ]
pheatmap::pheatmap(male_agg_mat,
scale=\row\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE)
pheatmap::pheatmap(female_agg_mat,
scale=\row\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE)
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#### expression of modules in mutant cells (side panels)
male
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```r
```r
## make monocle object with mutants
## extract data
mutant_cells_male <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$genotype_combined == \Mutant\ & tenx.mutant.integrated.sex@meta.data$sex == \male\),])
## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = mutant_cells_male)
## make new counts and pheno:
## the reason we use the integrated and then subsetted is because these cells have been normalised whereas the cells in pb_sex_filtered have not been normalised (well they have but with doublets in them)
data <- as(as.matrix(GetAssayData(seurat.object, assay = \RNA\, slot = \data\)), 'sparseMatrix')
pd <- data.frame(seurat.object@meta.data)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
monocle.object.mutants.male <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
monocle.object.mutants.male = preprocess_cds(monocle.object.mutants.male, num_dim = 50, norm_method = \none\)
### if using integrated data:
# norm_method = \none\, alignment_group = \~ experiment\
## make a cell group dataframe for aggregating expression values:
mutant_cell_group_df <- data.frame(cell = row.names(monocle.object.mutants.male@colData), cell_group = monocle.object.mutants.male@colData$identity_updated)
## aggregate expression
mutant_male_agg_mat <- aggregate_gene_expression(monocle.object.mutants.male, gene_module_df_sex, mutant_cell_group_df)
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plot
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```r
```r
mutant_male_agg_mat <- mutant_male_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_male_agg_mat)), ]
pheatmap::pheatmap(mutant_male_agg_mat,
scale=\column\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = TRUE) + theme(legend.position = \bottom\)
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```r
```r
mutant_male_agg_mat <- mutant_male_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_male_agg_mat)), ]
pheatmap::pheatmap(mutant_male_agg_mat,
scale=\column\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = TRUE) + theme(legend.position = \bottom\)
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female
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```r
```r
## make monocle object with mutants
## extract data
mutant_cells_female <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$genotype_combined == \Mutant\ & tenx.mutant.integrated.sex@meta.data$sex == \female\),])
## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = mutant_cells_female)
## make new counts and pheno:
## the reason we use the integrated and then subsetted is because these cells have been normalised whereas the cells in pb_sex_filtered have not been normalised (well they have but with doublets in them)
data <- as(as.matrix(GetAssayData(seurat.object, assay = \RNA\, slot = \data\)), 'sparseMatrix')
pd <- data.frame(seurat.object@meta.data)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
monocle.object.mutants.female <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
monocle.object.mutants.female = preprocess_cds(monocle.object.mutants.female, num_dim = 50, norm_method = \none\)
### if using integrated data:
# norm_method = \none\, alignment_group = \~ experiment\
## make a cell group dataframe for aggregating expression values:
mutant_cell_group_df <- data.frame(cell = row.names(monocle.object.mutants.female@colData), cell_group = monocle.object.mutants.female@colData$identity_updated)
## aggregate expression
mutant_female_agg_mat <- aggregate_gene_expression(monocle.object.mutants.female, gene_module_df_sex, mutant_cell_group_df)
<!-- rnb-source-end -->
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plot
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```r
```r
mutant_female_agg_mat <- mutant_female_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_female_agg_mat)), ]
pheatmap::pheatmap(mutant_female_agg_mat,
scale=\column\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = FALSE) + theme(legend.position = \bottom\)
<!-- rnb-source-end -->
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```r
```r
mutant_female_agg_mat <- mutant_female_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_female_agg_mat)), ]
pheatmap::pheatmap(mutant_female_agg_mat,
scale=\row\,
#clustering_method=\ward.D2\,
cluster_rows = FALSE,
cluster_cols = TRUE) + theme(legend.position = \bottom\)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### for particular genes (lower panel)
<!-- rnb-text-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300
list_of_mutant_genes <- c(\PBANKA-0828000\, \PBANKA-1302700\, \PBANKA-1447900\, \PBANKA-0102400\, \PBANKA-0716500\, \PBANKA-1435200\, \PBANKA-1418100\, \PBANKA-1144800\, \PBANKA-0902300\, \PBANKA-0413400\, \PBANKA-1454800\)
list_of_genes_of_interest <- c(\PBANKA-1437500\, \PBANKA-0909600\,\PBANKA-1034300\, \PBANKA-0828000\, \PBANKA-1302700\, \PBANKA-1447900\, \PBANKA-0102400\, \PBANKA-0716500\, \PBANKA-1435200\, \PBANKA-1418100\, \PBANKA-1144800\, \PBANKA-0902300\, \PBANKA-0413400\, \PBANKA-1454800\)
##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)))
## aggregate expression
## make plotting df
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, cell_group_df)
row.names(agg_mat_genes_of_interest) <- genes_of_interest$gene
#row.names(agg_mat_genes_of_interest) <- factor(row.names(agg_mat_genes_of_interest), levels = row.names(agg_mat_genes_of_interest)[module_dendro$order])
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,match(rownames(cluster_anno), colnames(agg_mat_genes_of_interest))]
pheatmap::pheatmap(agg_mat_genes_of_interest,
scale=\row\,
cluster_cols = FALSE,
cluster_rows = FALSE,
clustering_method=\ward.D2\,
annotation_col = cluster_anno,
annotation_colors = annotation_colours)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
complex heat map
<!-- rnb-text-end -->
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```r
```r
## aggregate gene expression
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, df_all_cells)
agg_mat_genes_of_interest <- as.matrix(agg_mat_genes_of_interest)
## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
column_order = NULL,
cluster_columns = FALSE,
cluster_rows = FALSE,
show_column_dend = FALSE,
column_labels = rep(\\, ncol(agg_mat_all_cells_matrix)),
#row_order = module_dendro$order,
clustering_method_columns = \ward.D2\,
bottom_annotation = heatmap_annotation,
col = colorRampPalette(rev(brewer.pal(n = 7, name =
\RdYlBu\)))(100),
heatmap_legend_param = list(direction = \horizontal\))
## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = \bottom\,
annotation_legend_side = \bottom\)
<!-- rnb-source-end -->
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```r
```r
## order cols in the matrix
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[ ,match(rownames(df_anno), colnames(agg_mat_genes_of_interest))]
## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
column_order = NULL,
cluster_columns = TRUE,
cluster_rows = FALSE,
show_column_dend = FALSE,
column_labels = rep(\\, ncol(agg_mat_all_cells_matrix)),
#row_order = module_dendro$order,
clustering_method_columns = \ward.D2\,
bottom_annotation = heatmap_annotation,
col = colorRampPalette(rev(brewer.pal(n = 7, name =
\RdYlBu\)))(100),
heatmap_legend_param = list(direction = \horizontal\))
## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = \bottom\,
annotation_legend_side = \bottom\)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Using Seurat to visualise cells
<!-- rnb-text-end -->
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```r
```r
# find markers for every cluster compared to all remaining cells, report only the positive ones
tenx.mutant.integrated.sex.markers <- FindAllMarkers(tenx.mutant.integrated.sex, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
tenx.mutant.integrated.sex.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)
<!-- rnb-source-end -->
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```r
```r
top10 <- tenx.mutant.integrated.sex.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(tenx.mutant.integrated.sex, features = top10$gene) + NoLegend()
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
But we also have the old pt values that we can use in the seurat object ie FeaturePlot(tenx.mutant.integrated.sex, reduction = "pca", pt.size = 0.01, features = "old_pt_values")
So let's plot a heatmap where we plot: (x) all cells vs. (y) genes arranged by module that they belong to.
add an old pt annotation to the top
prepare data:
<!-- rnb-text-end -->
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```r
```r
## extracts only 10x cells
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == \WT_10X\),])
## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = wt_cells)
<!-- rnb-source-end -->
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```r
```r
DoHeatmap(seurat.object, features = top10$gene) + NoLegend()
<!-- rnb-source-end -->
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```r
```r
## aggregate gene expression
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, df_all_cells)
agg_mat_genes_of_interest <- as.matrix(agg_mat_genes_of_interest)
## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
column_order = NULL,
cluster_columns = FALSE,
cluster_rows = FALSE,
show_column_dend = FALSE,
column_labels = rep(\\, ncol(agg_mat_all_cells_matrix)),
#row_order = module_dendro$order,
clustering_method_columns = \ward.D2\,
bottom_annotation = heatmap_annotation,
col = colorRampPalette(rev(brewer.pal(n = 7, name =
\RdYlBu\)))(100),
heatmap_legend_param = list(direction = \horizontal\))
## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = \bottom\,
annotation_legend_side = \bottom\)
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Expression of CCP2 and MG1 by each genotype and each sex
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```r
```r
# ccp2 - \PBANKA-1319500\ - female 820
# MG1 - \PBANKA-0416100\ - male 820
## make a custom dataframe:
marker_820_df <- as.data.frame(t(as.data.frame(tenx.mutant.integrated.sex@assays$RNA@data[c(\PBANKA-1319500\, \PBANKA-0416100\), ], stringsAsFactors=F)))
marker_820_df$cell_id <- row.names(marker_820_df)
sex_id <- data.frame(sex_at = tenx.mutant.integrated.sex@meta.data$at_sex, genotype = tenx.mutant.integrated.sex@meta.data$identity_updated ,cell_id = row.names(tenx.mutant.integrated.sex@meta.data))
marker_820_df <- merge(marker_820_df, sex_id, by = \cell_id\)
ggplot(marker_820_df, aes(fill=sex_at, y=`PBANKA-1319500`, x=genotype)) +
geom_violin() +
geom_jitter(shape=16, position=position_jitter(0.2)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90))
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```r
```r
#tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == \pre-det\ | tenx.mutant.integrated.sex@meta.data$sex == \female\), ]
VlnPlot(tenx.mutant.integrated.sex, group.by = \identity_updated\, split.by = \at_sex\, features = c(\PBANKA-1319500\), split.plot = TRUE)
VlnPlot(tenx.mutant.integrated.sex, group.by = \identity_updated\, split.by = \at_sex\, features = c(\PBANKA-0416100\), split.plot = TRUE)
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Differential expression
Re-cluster the data so we have very course grain clusters
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```r
```r
## find new clusters
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = 1, random.seed = 42, algorithm = 2)
## plot the graph
DimPlot(tenx.mutant.integrated.sex, reduction = \umapoptimised_post_repca\, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = \integrated_snn_res.1\) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\) +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
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```r
```r
VlnPlot(tenx.mutant.integrated.sex, features = c(\PT_Female_UMAP\, \PT_Male_UMAP\))
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#### Show representation of genotypes per cluster
prep for dotplot
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```r
```r
## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)
## define order for plotting
my_levels_sex <- c(\2\, \3\, \9\, \6\, \11\, \5\, \0\, \10\, \13\, \14\, \12\, \8\, \15\, \1\, \4\, \7\)
## redefine order of clusters:
df_meta_data$seurat_clusters <- factor(x = df_meta_data$seurat_clusters, levels = my_levels_sex)
## make a new df of CLUSTER and IDENTITY
dot_plot_df <- as.data.frame.matrix(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined))
dot_plot_df$cluster <- rownames(dot_plot_df)
## calculate percentage of cells for each genotype
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined), margin = 2)) * 100)
## make a column for cluster names
dot_plot_df_pc$cluster <- rownames(dot_plot_df_pc)
## melt dataframe for plotting
library(reshape2)
dot_plot_df_pc_melted <- melt(dot_plot_df_pc, variable.name = \cluster\)
colnames(dot_plot_df_pc_melted)[2] <- \identity\
## melt the raw number too
dot_plot_df_melted <- melt(dot_plot_df, variable.name = \cluster\)
colnames(dot_plot_df_melted)[2] <- \identity\
colnames(dot_plot_df_melted)[3] <- \raw_number\
## merge together
identical(dot_plot_df_melted$cluster, dot_plot_df_pc_melted$cluster)
dot_plot_merged <- cbind(dot_plot_df_melted, dot_plot_df_pc_melted)
dot_plot_merged <- dot_plot_merged[,c(1,2,3,6)]
## redefine order of clusters
dot_plot_merged$cluster <- factor(x = dot_plot_merged$cluster, levels = my_levels_sex)
## where values are zero, add NA
## find wells where it's zero
zero_values <- dot_plot_merged$value == 0
dot_plot_merged$value[zero_values] <- NA
## also do for raw number
zero_values <- dot_plot_merged$raw_number == 0
dot_plot_merged$raw_number[zero_values] <- NA
## reorder x axis:
my_levels_genotype <- c(\GCSKO-oom\, \GCSKO-29\, \GCSKO-2\, \GCSKO-19\, \GCSKO-3\, \GCSKO-21\, \GCSKO-13\, \GCSKO-28\, \GCSKO-10_820\, \GCSKO-17\, \GCSKO-20\, \WT\, \WT_10X\)
dot_plot_merged$identity <- factor(x = dot_plot_merged$identity, levels = my_levels_genotype)
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plot
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```r
```r
## plot
dot_plot_identity <- ggplot(dot_plot_merged, aes(y = factor(cluster), x = factor(identity))) +
## make into a dot plot
geom_point(aes(colour=value, size=raw_number)) +
scale_color_gradient(low=\blue\, high=\red\, limits=c( 0, max(dot_plot_df_pc_melted$value)), na.value=\white\) +
#change the colours
scale_colour_viridis(option = \inferno\, guide = \colourbar\, na.value=\white\) +
theme_classic() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), text=element_text(size=16, family=\Arial\)) +
ylab(\Cluster\) +
xlab(\Identity\) +
theme(axis.text.x=element_text(size=12, angle=45, hjust=1, vjust=1), axis.text.y=element_text(size=12,), legend.position = \bottom\, plot.margin = unit(c(1,3,1,3), \lines\)) +
## annotate males
geom_hline(aes(yintercept = 2.5)) +
## annotate females
geom_hline(aes(yintercept = 7.5)) +
## annotate pheno 1
geom_vline(aes(xintercept = 4.5)) +
## annotate pheno 2
geom_vline(aes(xintercept = 5.5)) +
## annotate pheno 3
geom_vline(aes(xintercept = 7.5)) +
## annotate pheno 4
geom_vline(aes(xintercept = 11.5))
## print
print(dot_plot_identity)
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cut off and plot
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```r
```r
pre_branch_cells <- c(2,3)
inmature_male_cells <- c()
inmature_female_cells <-
mature_male_cells <-
mature_female_cells <-
## plot the graph
DimPlot(tenx.mutant.integrated.sex, reduction = \umapoptimised_post_repca\, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = \integrated_snn_res.1\) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\)
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```r
```r
wt_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == \WT\ | tenx.mutant.integrated.sex@meta.data$identity_combined == \WT_10X\), ])
male_inmature_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == \WT\ | tenx.mutant.integrated.sex@meta.data$identity_combined == \WT_10X\), ])
early_wt_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == \WT\ && tenx.mutant.integrated.sex@meta.data$PT_LineageFemale < 46), ])
FindMarkers(tenx.mutant.integrated.sex, cells.1 = , cells.2 = )
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### Generate New Clusters
We must now recalculate the clusters to gain a better understanding of the heterogeneity of the subset. Since using the previous clusters, the asexual cells obscured some of the variation.
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```r
```r
## copy old clusters
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, tenx.mutant.integrated.sex@meta.data$seurat_clusters, col.name = \pre_sex_clusters\)
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```r
```r
## generate new clusters at various resolutions
tenx.mutant.integrated.sex <- FindNeighbors(tenx.mutant.integrated.sex, dims = 1:15)
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = c(2,3,4,5,6), random.seed = 42, algorithm = 2)
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### Visualise
#### Choose Cluster Resolution
View the clusters at different resolutions to chose the appropraite resolution
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```r
```r
## plot
DimPlot(tenx.mutant.integrated.sex, reduction = \umapoptimised_post_repca\, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = \integrated_snn_res.2\) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\) +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
DimPlot(tenx.mutant.integrated.sex, reduction = \umapoptimised_post_repca\, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = \integrated_snn_res.3\) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\) +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
DimPlot(tenx.mutant.integrated.sex, reduction = \umapoptimised_post_repca\, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = \integrated_snn_res.4\) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\) +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
DimPlot(tenx.mutant.integrated.sex, reduction = \umapoptimised_post_repca\, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = \integrated_snn_res.5\) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\) +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
DimPlot(tenx.mutant.integrated.sex, reduction = \umapoptimised_post_repca\, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = \integrated_snn_res.6\) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\) +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
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Go with 4 clusters
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```r
```r
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = 4, random.seed = 42, algorithm = 2)
DimPlot(tenx.mutant.integrated.sex, reduction = \umapoptimised_post_repca\, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\) +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
<!-- rnb-source-end -->
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#### Original UMAP
You can also use the original UMAP projection
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```r
```r
DimPlot(tenx.mutant.integrated.sex, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5) +
coord_fixed() +
theme_void() +
theme(legend.position = \bottom\) +
guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
<!-- rnb-source-end -->
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#### Representation
look at cluster representation
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plot
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```r
```r
## this function writes the next bit of code for you
ploty <- c()
for(i in seq_along(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters))){
ploty <- paste0(ploty, \list_UMAPs_by_cluster[[\, i, \]]\, \ + \)
}
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```r
```r
## plot
library(gridExtra)
grid.arrange(list_UMAPs_by_cluster[[1]] , list_UMAPs_by_cluster[[2]] , list_UMAPs_by_cluster[[3]] , list_UMAPs_by_cluster[[4]] , list_UMAPs_by_cluster[[5]] , list_UMAPs_by_cluster[[6]] , list_UMAPs_by_cluster[[7]] , list_UMAPs_by_cluster[[8]] , list_UMAPs_by_cluster[[9]] , list_UMAPs_by_cluster[[10]] , list_UMAPs_by_cluster[[11]] , list_UMAPs_by_cluster[[12]] , list_UMAPs_by_cluster[[13]] , list_UMAPs_by_cluster[[14]] , list_UMAPs_by_cluster[[15]] , list_UMAPs_by_cluster[[16]] , list_UMAPs_by_cluster[[17]] , list_UMAPs_by_cluster[[18]] , list_UMAPs_by_cluster[[19]] , list_UMAPs_by_cluster[[20]] , list_UMAPs_by_cluster[[21]] , list_UMAPs_by_cluster[[22]] , list_UMAPs_by_cluster[[23]] , list_UMAPs_by_cluster[[24]] , list_UMAPs_by_cluster[[25]] , list_UMAPs_by_cluster[[26]] , list_UMAPs_by_cluster[[27]] , list_UMAPs_by_cluster[[28]] , list_UMAPs_by_cluster[[29]] , list_UMAPs_by_cluster[[30]] , list_UMAPs_by_cluster[[31]], ncol = 5)
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```r
```r
## for loop which takes each cluster and makes a list of cells and then plots a highlighted plot and adds it to a list
## make a df of the number of
n_per_cluster <- as.data.frame(table(tenx.mutant.integrated.sex@meta.data$seurat_clusters))
rownames(n_per_cluster) <- n_per_cluster$Var1
n_per_cluster <- n_per_cluster[,2]
## make a blank list
list_UMAPs_by_cluster <- vector(mode = \list\, length = length(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)))
## for loop
for(i in seq_along(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters))){
## make a list of cells
list_of_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$seurat_clusters == levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)[i]), ])
umap_plot <- DimPlot(tenx.mutant.integrated.sex, reduction = \umap\, label = FALSE, repel = TRUE, pt.size = 0.1, cells.highlight = list_of_cells) +
coord_fixed() +
scale_color_manual(values=c(\#000000\, \#f54e1e\)) +
theme_void() +
labs(title = paste(\cluster\, levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)[i], \\n\, \(n = \, n_per_cluster[i],\)\)) +
theme(plot.title = element_text(hjust = 0.5), legend.position = \none\)
## add to the list
list_UMAPs_by_cluster[[i]] <- umap_plot
}
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```r
```r
## plot
grid.arrange(list_UMAPs_by_cluster[[1]] , list_UMAPs_by_cluster[[2]] , list_UMAPs_by_cluster[[3]] , list_UMAPs_by_cluster[[4]] , list_UMAPs_by_cluster[[5]] , list_UMAPs_by_cluster[[6]] , list_UMAPs_by_cluster[[7]] , list_UMAPs_by_cluster[[8]] , list_UMAPs_by_cluster[[9]] , list_UMAPs_by_cluster[[10]] , list_UMAPs_by_cluster[[11]] , list_UMAPs_by_cluster[[12]] , list_UMAPs_by_cluster[[13]] , list_UMAPs_by_cluster[[14]] , list_UMAPs_by_cluster[[15]] , list_UMAPs_by_cluster[[16]] , list_UMAPs_by_cluster[[17]] , list_UMAPs_by_cluster[[18]] , list_UMAPs_by_cluster[[19]] , list_UMAPs_by_cluster[[20]] , list_UMAPs_by_cluster[[21]] , list_UMAPs_by_cluster[[22]] , list_UMAPs_by_cluster[[23]] , list_UMAPs_by_cluster[[24]] , list_UMAPs_by_cluster[[25]] , list_UMAPs_by_cluster[[26]] , list_UMAPs_by_cluster[[27]] , list_UMAPs_by_cluster[[28]] , list_UMAPs_by_cluster[[29]] , list_UMAPs_by_cluster[[30]] , list_UMAPs_by_cluster[[31]], ncol = 5)
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#### Show correspondance with old clusters (Alluvium plot, Sankey diagram)
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```r
```r
##This is the set up for this:
## two clusters that differ
table(tenx.mutant.integrated.sex@meta.data$seurat_clusters, tenx.mutant.integrated.sex@meta.data$pre_sex_clusters)
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```r
```r
## hemberg uses gvisSankey in https://github.com/hemberg-lab/scmap/blob/3aa2bb487a80a946469393857cea6a6effc618fb/R/Utils.R code - so maybe update with this?
## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)
df_alluvial <- melt(table(data.frame(full_clusters = df_meta_data$pre_sex_clusters, sex_clusters = df_meta_data$seurat_clusters)))
## load required package
#library(ggalluvial)
## plot
ggplot(df_alluvial, aes(y = value, axis1 = full_clusters, axis2 = sex_clusters)) +
geom_alluvium(aes(fill = sex_clusters),
width = 0, knot.pos = 0, reverse = FALSE) +
guides(fill = FALSE) +
geom_stratum(width = 1/8, reverse = FALSE) +
geom_text(stat = \stratum\, infer.label = TRUE, reverse = FALSE) +
scale_x_continuous(breaks = 1:2, labels = c(\original cluster\, \Sex Cluster\)) +
coord_flip() +
ggtitle(\Cluster Identiity in full dataset vs. sex only\) +
theme_classic()
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#### Show representation of genotypes per cluster
prep for dotplot
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```r
```r
## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)
## define order for plotting
my_levels_sex <- c(\0\, \9\, \28\, \2\, \29\, \16\, \22\, \23\, \15\, \21\, \30\, \4\, \3\, \18\, \7\, \8\, \6\, \20\, \12\, \26\, \13\, \5\, \11\, \25\, \1\, \17\, \10\, \24\, \14\, \27\, \19\)
## redefine order of clusters:
df_meta_data$seurat_clusters <- factor(x = df_meta_data$seurat_clusters, levels = my_levels_sex)
## make a new df of CLUSTER and IDENTITY
dot_plot_df <- as.data.frame.matrix(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined))
dot_plot_df$cluster <- rownames(dot_plot_df)
## calculate percentage of cells for each genotype
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined), margin = 2)) * 100)
## make a column for cluster names
dot_plot_df_pc$cluster <- rownames(dot_plot_df_pc)
## melt dataframe for plotting
library(reshape2)
dot_plot_df_pc_melted <- melt(dot_plot_df_pc, variable.name = \cluster\)
colnames(dot_plot_df_pc_melted)[2] <- \identity\
## melt the raw number too
dot_plot_df_melted <- melt(dot_plot_df, variable.name = \cluster\)
colnames(dot_plot_df_melted)[2] <- \identity\
colnames(dot_plot_df_melted)[3] <- \raw_number\
## merge together
identical(dot_plot_df_melted$cluster, dot_plot_df_pc_melted$cluster)
dot_plot_merged <- cbind(dot_plot_df_melted, dot_plot_df_pc_melted)
dot_plot_merged <- dot_plot_merged[,c(1,2,3,6)]
## redefine order of clusters
dot_plot_merged$cluster <- factor(x = dot_plot_merged$cluster, levels = my_levels_sex)
## where values are zero, add NA
## find wells where it's zero
zero_values <- dot_plot_merged$value == 0
dot_plot_merged$value[zero_values] <- NA
## also do for raw number
zero_values <- dot_plot_merged$raw_number == 0
dot_plot_merged$raw_number[zero_values] <- NA
## reorder x axis:
my_levels_genotype <- c(\GCSKO-oom\, \GCSKO-29\, \GCSKO-2\, \GCSKO-19\, \GCSKO-3\, \GCSKO-21\, \GCSKO-13\, \GCSKO-28\, \GCSKO-10_820\, \GCSKO-17\, \GCSKO-20\, \WT\, \WT_10X\)
dot_plot_merged$identity <- factor(x = dot_plot_merged$identity, levels = my_levels_genotype)
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plot
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```r
```r
## plot
dot_plot_identity <- ggplot(dot_plot_merged, aes(y = factor(cluster), x = factor(identity))) +
## make into a dot plot
geom_point(aes(colour=value, size=raw_number)) +
scale_color_gradient(low=\blue\, high=\red\, limits=c( 0, max(dot_plot_df_pc_melted$value)), na.value=\white\) +
#change the colours
scale_colour_viridis(option = \inferno\, guide = \colourbar\, na.value=\white\) +
theme_classic() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), text=element_text(size=16, family=\Arial\)) +
ylab(\Cluster\) +
xlab(\Identity\) +
theme(axis.text.x=element_text(size=12, angle=45, hjust=1, vjust=1), axis.text.y=element_text(size=12,), legend.position = \bottom\, plot.margin = unit(c(1,3,1,3), \lines\)) +
## annotate males
geom_hline(aes(yintercept = 5.5)) +
## annotate females
geom_hline(aes(yintercept = 20.5)) +
## annotate pheno 1
geom_vline(aes(xintercept = 4.5)) +
## annotate pheno 2
geom_vline(aes(xintercept = 5.5)) +
## annotate pheno 3
geom_vline(aes(xintercept = 7.5)) +
## annotate pheno 4
geom_vline(aes(xintercept = 11.5))
## print
print(dot_plot_identity)
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# Save and Export {.tabset}
save environment
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```r
```r
## This saves everything in the global environment for easy recall later
#save.image(file = \GCSKO_Sex_Branch_Analysis.RData\)
#load(file = \GCSKO_Sex_Branch_Analysis.RData\)
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save modules
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```r
```r
#gene_module_df_sex
write.csv(gene_module_df_sex, file = \../data_to_export/gene_module_df_sex.csv\)
#save(pb_30k_sex_filtered, pb_sex_filtered, file = \Part_2_input.Rdata\)
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Save object(s)
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```r
```r
## save integrated object to file
#saveRDS(tenx.mutant.integrated.sex, file = \../data_to_export/tenx.mutant.integrated.sex.processed.RDS\)
## restore the object
#tenx.mutant.integrated <- readRDS(\../data_to_export/tenx.mutant.integrated.sex.processed.RDS\)
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# Appendix {.tabset}
### Useful info
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```r
## The tree for monocle is located here:
# monocle.object@principal_graph_aux[["UMAP"]]$dp_mst
## see more info below in getAnywhere(learn_graph)
## in order to get a plottable dataframe of this:
#test <- as.data.frame(t(as.data.frame(monocle.object@principal_graph_aux[["UMAP"]]$dp_mst)))
#ggplot(test, aes(x = DIMUMAP_1, y = DIMUMAP_2)) + geom_point()
## the curves for slingshot are located here:
#as.data.frame(crv1@curves$curve1$s)
## so a plot can be made by:
#ggplot(as.data.frame(crv1@curves$curve3$s), aes(x= DIMUMAP_1, y = DIMUMAP_2)) + geom_line()Functions Info
```r
Seurat:::DoHeatmap
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```r
```r
monocle:::plot_pseudotime_heatmap
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```r
## to understand how the line is stored
getAnywhere(learn_graph)A single object matching ‘learn_graph’ was found
It was found in the following places
package:monocle3
namespace:monocle3
with value
function (cds, use_partition = TRUE, close_loop = TRUE, learn_graph_control = NULL,
verbose = FALSE)
{
reduction_method <- "UMAP"
if (!is.null(learn_graph_control)) {
assertthat::assert_that(methods::is(learn_graph_control,
"list"))
assertthat::assert_that(all(names(learn_graph_control) %in%
c("euclidean_distance_ratio", "geodesic_distance_ratio",
"minimal_branch_len", "orthogonal_proj_tip",
"prune_graph", "scale", "ncenter", "rann.k",
"maxiter", "eps", "L1.gamma", "L1.sigma")), msg = "Unknown variable in learn_graph_control")
}
euclidean_distance_ratio <- ifelse(is.null(learn_graph_control$euclidean_distance_ratio),
1, learn_graph_control$euclidean_distance_ratio)
geodesic_distance_ratio <- ifelse(is.null(learn_graph_control$geodesic_distance_ratio),
1/3, learn_graph_control$geodesic_distance_ratio)
minimal_branch_len <- ifelse(is.null(learn_graph_control$minimal_branch_len),
10, learn_graph_control$minimal_branch_len)
orthogonal_proj_tip <- ifelse(is.null(learn_graph_control$orthogonal_proj_tip),
FALSE, learn_graph_control$orthogonal_proj_tip)
prune_graph <- ifelse(is.null(learn_graph_control$prune_graph),
TRUE, learn_graph_control$prune_graph)
ncenter <- learn_graph_control$ncenter
scale <- ifelse(is.null(learn_graph_control$scale), FALSE,
learn_graph_control$scale)
rann.k <- ifelse(is.null(learn_graph_control$rann.k), 25,
learn_graph_control$rann.k)
maxiter <- ifelse(is.null(learn_graph_control$maxiter), 10,
learn_graph_control$maxiter)
eps <- ifelse(is.null(learn_graph_control$eps), 1e-05, learn_graph_control$eps)
L1.gamma <- ifelse(is.null(learn_graph_control$L1.gamma),
0.5, learn_graph_control$L1.gamma)
L1.sigma <- ifelse(is.null(learn_graph_control$L1.sigma),
0.01, learn_graph_control$L1.sigma)
assertthat::assert_that(methods::is(cds, "cell_data_set"))
assertthat::assert_that(reduction_method %in% c("UMAP"),
msg = paste0("unsupported or invalid reduction method '",
reduction_method, "'"))
assertthat::assert_that(is.logical(use_partition))
assertthat::assert_that(is.logical(close_loop))
assertthat::assert_that(is.logical(verbose))
assertthat::assert_that(is.logical(orthogonal_proj_tip))
assertthat::assert_that(is.logical(prune_graph))
assertthat::assert_that(is.logical(scale))
assertthat::assert_that(is.numeric(euclidean_distance_ratio))
assertthat::assert_that(is.numeric(geodesic_distance_ratio))
assertthat::assert_that(is.numeric(minimal_branch_len))
if (!is.null(ncenter)) {
assertthat::assert_that(assertthat::is.count(ncenter))
}
assertthat::assert_that(assertthat::is.count(maxiter))
assertthat::assert_that(assertthat::is.count(rann.k))
assertthat::assert_that(is.numeric(eps))
assertthat::assert_that(is.numeric(L1.sigma))
assertthat::assert_that(is.numeric(L1.sigma))
assertthat::assert_that(!is.null(reducedDims(cds)[[reduction_method]]),
msg = paste("No dimensionality reduction for", reduction_method,
"calculated.", "Please run reduce_dimension with",
"reduction_method =", reduction_method, "and cluster_cells before running",
"learn_graph."))
assertthat::assert_that(!is.null(cds@clusters[[reduction_method]]),
msg = paste("No cell clusters for", reduction_method,
"calculated.", "Please run cluster_cells with", "reduction_method =",
reduction_method, "before running learn_graph."))
if (use_partition) {
partition_list <- cds@clusters[[reduction_method]]$partitions
}
else {
partition_list <- rep(1, nrow(colData(cds)))
}
multi_tree_DDRTree_res <- multi_component_RGE(cds, scale = scale,
reduction_method = reduction_method, partition_list = partition_list,
irlba_pca_res = reducedDims(cds)[[reduction_method]],
max_components = max_components, ncenter = ncenter, rann.k = rann.k,
maxiter = maxiter, eps = eps, L1.gamma = L1.gamma, L1.sigma = L1.sigma,
close_loop = close_loop, euclidean_distance_ratio = euclidean_distance_ratio,
geodesic_distance_ratio = geodesic_distance_ratio, prune_graph = prune_graph,
minimal_branch_len = minimal_branch_len, verbose = verbose)
rge_res_W <- multi_tree_DDRTree_res$ddrtree_res_W
rge_res_Z <- multi_tree_DDRTree_res$ddrtree_res_Z
rge_res_Y <- multi_tree_DDRTree_res$ddrtree_res_Y
cds <- multi_tree_DDRTree_res$cds
dp_mst <- multi_tree_DDRTree_res$dp_mst
principal_graph(cds)[[reduction_method]] <- dp_mst
cds@principal_graph_aux[[reduction_method]]$dp_mst <- rge_res_Y
cds <- project2MST(cds, project_point_to_line_segment, orthogonal_proj_tip,
verbose, reduction_method, rge_res_Y)
cds
}
<bytecode: 0x7f8e93d974a8>
<environment: namespace:monocle3>getAnywhere(aggregate_gene_expression)A single object matching ‘aggregate_gene_expression’ was found
It was found in the following places
package:monocle3
namespace:monocle3
with value
function (cds, gene_group_df = NULL, cell_group_df = NULL, norm_method = c("log",
"binary", "size_only"), pseudocount = 1, scale_agg_values = TRUE,
max_agg_value = 3, min_agg_value = -3, exclude.na = TRUE)
{
if (is.null(gene_group_df) && is.null(cell_group_df))
stop("Error: one of either gene_group_df or cell_group_df must not be NULL")
agg_mat <- normalized_counts(cds, norm_method = norm_method,
pseudocount = pseudocount)
if (is.null(gene_group_df) == FALSE) {
gene_group_df <- as.data.frame(gene_group_df)
gene_group_df <- gene_group_df[gene_group_df[, 1] %in%
fData(cds)$gene_short_name | gene_group_df[, 1] %in%
row.names(fData(cds)), , drop = FALSE]
short_name_mask <- gene_group_df[[1]] %in% fData(cds)$gene_short_name
if (any(short_name_mask)) {
geneids <- as.character(gene_group_df[[1]])
geneids[short_name_mask] <- row.names(fData(cds))[match(geneids[short_name_mask],
fData(cds)$gene_short_name)]
gene_group_df[[1]] <- geneids
}
agg_mat = as.matrix(my.aggregate.Matrix(agg_mat[gene_group_df[,
1], ], as.factor(gene_group_df[, 2]), fun = "sum"))
if (scale_agg_values) {
agg_mat <- t(scale(t(agg_mat)))
agg_mat[agg_mat < min_agg_value] <- min_agg_value
agg_mat[agg_mat > max_agg_value] <- max_agg_value
}
}
if (is.null(cell_group_df) == FALSE) {
cell_group_df <- as.data.frame(cell_group_df)
cell_group_df <- cell_group_df[cell_group_df[, 1] %in%
row.names(pData(cds)), , drop = FALSE]
agg_mat <- agg_mat[, cell_group_df[, 1]]
agg_mat <- my.aggregate.Matrix(Matrix::t(agg_mat), as.factor(cell_group_df[,
2]), fun = "mean")
agg_mat <- Matrix::t(agg_mat)
}
if (exclude.na) {
agg_mat <- agg_mat[rownames(agg_mat) != "NA", colnames(agg_mat) !=
"NA", drop = FALSE]
}
return(agg_mat)
}
<bytecode: 0x7f8cd3e0ff18>
<environment: namespace:monocle3>so essentially it first takes the counts matrix and subsets it by your gene groups, then it sums the values - if you specify scale then it will calculate the z score of the transformed dataframe
A. Diffusion Map
construct map
```r
## construct diffusion map
## http://www.bioconductor.org/packages/devel/bioc/vignettes/slingshot/inst/doc/vignette.html
## input is a transformed expression matrix (genes as cols and cells as rows)
dm <- DiffusionMap(t(as.data.frame(tenx.mutant.integrated.sex@assays$integrated@data)))
## extract meta data for plotting
df_meta_data <- (as.data.frame(tenx.mutant.integrated.sex@meta.data))
## make combined dataframe
rd2 <- as.data.frame(cbind(DC1 = dm$DC1, DC2 = dm$DC2, identity = as.factor(as.character(tenx.mutant.integrated.sex@meta.data$post_integration_clusters))))
## plot
ggplot(rd2, aes(x = DC1, y = DC2, colour = as.character(identity))) + geom_point(size = 1) +
theme(axis.ticks.y = element_blank()) +
theme_classic()
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
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```r
```r
## make a density plot for real time correlations using kasia data
## make an annotation dataframe
anno_real_time <- data.frame(monocle.object@colData$cluster_colours_figure, monocle.object@colData$pt, monocle.object@colData$Prediction.Spearman._Kasia, row.names = rownames(monocle.object@colData))
names(anno_real_time) <- c(\sex\, \Pseudotime\, \real_time\)
## change the order of the cols (cells) in data frame
col.order <- rownames(anno_real_time[with(anno_real_time, order(sex, Pseudotime)), ])
anno_real_time <- anno_real_time[col.order,]
## add an \order\ col to the dataframe to assist in plotting
anno_real_time$order <- c(1:nrow(anno_real_time))
#### plot density plot x
dens1 <- ggplot(anno_real_time, aes(x = order, fill = as.factor(real_time))) +
geom_density(alpha = 0.2) +
#theme_void() +
#theme(legend.position = \none\)
## add annotations for sex
geom_rect(data = cluster_anno, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2, fill=Median_Pseudotime_of_Cluster), inherit.aes = FALSE) +
scale_fill_viridis_c(option = \plasma\) +
## flip coordinates
## geoms below will use another color scale
new_scale_fill() +
geom_rect(data = cluster_anno, mapping=aes(xmin=Bx1, xmax=Bx2, ymin=By1, ymax=By2, fill=Identity), inherit.aes = FALSE) +
scale_fill_manual(values = c(\Male\ =\#016c00\, \Female\=\#a52b1e\, \Asexual\= \#0052c5\, \Committed\ = \#f2eb23\))
dens1
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### TO PUT BACK IN
### A.
Put this in after you have decided what is actually male and female, also add a heatmap version below and figure out expression values (e.g. it's a Z-score at the moment)
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-plot-begin eyJoZWlnaHQiOjEwLCJ3aWR0aCI6MTAsInNpemVfYmVoYXZpb3IiOjEsImNvbmRpdGlvbnMiOltdfQ== -->
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA8AAAAPACAYAAAD61hCbAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAAA4ZVhJZk1NACoAAAAIAAGHaQAEAAAAAQAAABoAAAAAAAKgAgAEAAAAAQAAA8CgAwAEAAAAAQAAA8AAAAAAAuG0PAAAQABJREFUeAHsXQd8VMX2Pluy6YWEJHQSqnQQEFEsiKCC+Oy9oFgQFcXC81mx9/YUC4pPsIIFGyoqTQWl995C7wnp2f4/3yyzuXv3bol/hISd8/vd3Htn5s6d++1md78553xj8rKRMoWAQkAhoBBQCCgEFAIKAYWAQkAhoBBQCBzjCJiP8edTj6cQUAgoBBQCCgGFgEJAIaAQUAgoBBQCCgGBgCLA6o2gEFAIKAQUAgoBhYBCQCGgEFAIKAQUAjGBgCLAMfEyq4dUCCgEFAIKAYWAQkAhoBBQCCgEFAIKAUWA1XtAIaAQUAgoBBQCCgGFgEJAIaAQUAgoBGICAUWAY+JlVg+pEFAIKAQUAgoBhYBCQCGgEFAIKAQUAooAq/eAQkAhoBBQCCgEFAIKAYWAQkAhoBBQCMQEAooAx8TLrB5SIaAQUAgoBBQCCgGFgEJAIaAQUAgoBBQBVu8BhYBCQCGgEFAIKAQUAgoBhYBCQCGgEIgJBBQBjomXWT2kQkAhoBBQCCgEFAIKAYWAQkAhoBBQCCgCrN4DCgGFgEJAIaAQUAgoBBQCCgGFgEJAIRATCCgCHBMvs3pIhYBCQCGgEFAIKAQUAgoBhYBCQCGgEFAEWL0HFAIKAYWAQkAhoBBQCCgEFAIKAYWAQiAmEFAEOCZeZvWQCgGFgEJAIaAQUAgoBBQCCgGFgEJAIaAIsHoPKAQUAgoBhYBCQCGgEFAIKAQUAgoBhUBMIKAIcEy8zOohFQIKAYWAQkAhoBBQCCgEFAIKAYWAQkARYPUeUAgoBBQCCgGFgEJAIaAQUAgoBBQCCoGYQMAaE095BB5y27ZtVFJSEnCn+Ph4SktLo5ycnIDygoICKi8v95eZTCbKzs4Wm78wxMH69evJ7XbTcccdZ9hi69atVFpaSu3atSOzOXh+Y+fOnaK+bdu24nqMxel0UuvWrYP6s9vttGHDBsL40B/2Wos0Fm3bUMfAYdOmTdSpUyfDJvv376c9e/ZQXl4eJScnG7ZBIZ4Zzw4c9XjrL8K4MzMzKSsrS18VcL5582Zxz1D9bdy4UfRTr169gOu0JxgXxp+fn08Wi0VbpY4VAgoBhYBCQCGgEFAIKAQUAgqBI4yAIsCHCfC33nqLfv/9d8PeQH6GDx9OJ554oqh/6aWXaMmSJUFtQfCuuOIKuu6664LqUAAiNXToUPJ6vfTee++RJLHaxmPGjKE5c+bQHXfcQZdeeqm2Shx/8MEHNHv2bJoyZYo4x1j27t1LEydODGhbVVVFo0aNEuO87777qH379gH10Ywl4AKDExD5Rx55hHbs2EGfffZZQAvUPfnkk/Trr7+KcpDvPn36iPYJCQn+tiDQTz/9NP3222/+stNOO40ee+wxQ8K5ePFiGjlyJD3wwAM0YMAA/zX6g+XLl9Ptt99OV199Nd10000B1V988YUYLzCA9ejRQ4xVS9CB6fPPP08LFiwQExaJiYl0//330xlnnBHQlzpRCCgEFAIKAYWAQkAhoBBQCCgEjhwCigAfRqxBcl5++WV/jw6Hg+BxHTt2LD388MM0btw4atasmaiH9xEEDwYP7MGDB2nmzJmC2IJIXXzxxaJO++eHH34Q3k2bzUaTJ08WhEpbrz3GPXv37k1NmzbVFkd1LMnv0qVL6cEHH6Szzjor6LqajCXoYi4AcQVW8+bNo8aNGwc1+emnn2j69OniGU899VQCcX3qqacEZhI3XPToo48SyOqLL74oSPrUqVPp9ddfF8RZP+758+fT6NGjBSENuqGmoKKiQtzH4/FoSn2HK1euFP3fdtttNHjwYII3GSQe48AYpOF43759NGHCBIKH+P333xf3hje5Y8eOslnQ/uabbxZe5WeffTaorjYW2Ke9QY5Z75EpOTPs8LyOCjIlpFLy8EncNrTHPGwnx3Cl47t/k2vVt2ROzo74lF5nBcUNGE3WDudGbBuLDew8obb65hvJlJhEFv5M1pubP3u8Dju1HfMWJTZvrq9W5zoEyg6U0Ke3vEkHdxSShycmzRYzOSsddMOn91KDdjX/ftF1H3OnGxZsoh2rd1JiagJ16teB98Hv0ZgDRT3wEUXA63FT+TOnkNdeQZScwdF9vmhBr5d/8xzcSabUHEq+9xcy2dR7M9oXZueaHbTm9zVUVVZF+d3zqd2pgU6jaPtR7Y4cAooAH0asrVZrELk5/vjjqX79+gQv6h9//EFXXnmluCPCo/VEqG/fvrRq1SqaMWNGEAGG1/fHH3+kXr16CUIFjyk8lCkpKUFPkJqaSi6Xi5555hl64403DEOhgy46VCDJ77JlywRhw5j0VpOx6K/FObyi8NqCaIKg64kmxj5+/HgC8R00aJDoAse4Dp5rhGYDv3Xr1tHcuXOFtxe4wDBxgLBteMElAUZ7kG2QdoRy60PVxYWaP6+++iqlp6fT7t27NaW+w1deeUV43qV3vXPnzsJDDMK6ZcsWas4/qOFh//PPP+mFF17wT3iMGDFCRAjAe6x/3bU3Wbt2LeXm5mqLau2xt7yI7D+9RFRZQt7CbRHH6Y1LIPusdylh4KiIbWOpgWffOnIt/oSoqpg8xTuienTn96PI0m4gmQzSHKLq4BhutH7UveTgCIxItunRR6jDB+MjNYvp+oqDZfRoy1sMMXih9yi67cdHqUXv4wzrVWEwAv+75yP6dex0cjndZI2zkL3CQe9seY2ymoRPxwnuSZXs31lE0z+eTfFJ8XTe8DODUrQUQqERqPz4DvLsWU8Ewlu0jby6pl7+Tq+cdB8lXf2GrkadGiGw8NsF9N/LXhWTgx63z3HS+7KTaNgHw42aq7JagoBv2qeWDOZYHQZIEQze4EiG8F54kvW2aNEi2rVrF5188snUr18/QQJBiI0sIyODhg0bJjyjn3/+uVETwzKQXxD1FStWCA+oEfnFhTUZi9GNMG6Eb//vf/8zzGXGRMJHH30U5OFGji+IL+phP//8s8jj1YcVI9QYIdDS4IkFUb7nnnuEt1aWG+3hhYfn+aGHHgr6QgVxBkFFiLXWTjnlFHGKe8BA1PEa9uzZU5zjjwzh/uuvv/xldf3AtQ4h/6boH8NZRa5F30TfPkZautdPJ3LZa/S0Xgd7MZk4KwtEoJInoexRfM7iqqotBWTnz1RloRH4bcwPZGGiFsp+eOyzUFWqXIfAz+9Moymv/iRIr5sJMMgv7D8njSanw6VrrU7DIVBeUkHX5N1FHzz8Bb05YgJdmHlL0ER6uOtjuc5rLyfXsh985DcUEG4nuZZ+z5Ey7CFWFhaB3et3CfKLRpL84njeV3PptwmzcKisliKgPMBH4IX55ZdfxF2QCywNOa5FRUXiFB7Q4uJiEbaLfFiE1+rt+++/JxDbE044QRBAEMhvv/2WLrnkEn1TcX7BBRfQrFmzRPg1QqFl6LVhYy6U5Be5yfAcI982lNV0LPp+7rrrLoKXOpwhzBsbcEKYMZ5l4cKFIpdaikkhz7ZVq1YEsSrkNq9Zs0Z4T5FHjWeWhrBj5DiDPG/fvl0WB+0huIW8XeBvhBdeGxg8+lqD0FlcXBwVFhaKYtwDYc9ynLItrkPot/Rgy3J4lRHiDUMfdcUD7CliLO1l8jGi2nuKFeHQA+Xdv4EJcJW+OMK5l7wl/H7MVd43LVD2bVsx26QtCn3M3nOQ4PiGDUO3ifGaDX+sIpC1ULZrJeOtLCoEFv0YrPuBCx0cTr5/y35q2LpBVP2oRkQTHv1SEF6P0+e7RFTagqnL6YRzuih4IiDg2V9AJktckNdXfxnaePZvIUujdvoqda5BYMPcDZSQkiBCnzXF4nNzxa/L6dRrAx0m2jbq+OgioAjwYcK/srJSkEiIYUlDKC+8jxDHAgk755xzRBXIJgSUzjvvPNnUv4cAlhTLkoVQEobI0/nnny/IHkjhwIEDSRKnbt26yaaEcYBg4QsBnlD0B0ILcSyoQiPsGNdLw1hQBs8v8lthIJogwOhHrwJdk7HIe+j3WvILjJArHcrgMcVzwDp06BCAGQgwnheEtUmTJtS9e3cR+gzxLuTknnnmmeI6EGkYPLhQbjYy4IUcY4h9YfIAhjIQVmnyGOHRMK0KNEgwCDQM7WQbrQo02sAOHDhAjRo1Esf4k5SU5J8QMFLu9jesdQdREo1aN+5aNqBD+Vc1H5XCPwgzkF/+v43K0OxvYx/VHep8I73yf9ADRTvZEHRh7BVY44x/brkcbn4bqv/lmrwjrDbGUrz3fP/r5cWVCsMaAIjfNpGMp1i5SeR2kfo51uuhiRDq/xd1ymovAsafyLV3vLV2ZPBCQszqk08+CRoj8nRvueUWQXRQGcoLCY8hcl9Bmv/zn//4+4ESMkgi1IZvuOEGUY6cVoQCf/3116QlwAUFBcKzjFxT5KlCfRpKz5MmTaLLL79cEFoQXmkYS1lZmfCy/ve//6Uvv/yScC1yWyG0BY+wVgU62rFA1Et6ROW9kO8LT6k0EHHkGqOt3jBG5NBKFWjUw8MLjzcEpRo0aCCIO3AHgVy9erXYQDzhfYUQFkLF8QMOJPm1117zK1+jLzwnQpnhFYYhVBw5xcAfBmEteOZRJk0SaeRyQ+RKqwKNtnJiAe1wz3vvvTdABRrjgcl2sl+8N6Tpw6tleW3cm+s3J576JKoojnp45nrBgmdRX3yMNjRltyaK47QHZ2WNntBUr2mN2sdC48QWLaMnwCwEk9iiRSzA8refse0ZnWnbok3krDKepGzSNf9v9x1rF/Y8rzv99dX8oMeGaE79ZioHOAiYMAWX3DuIJj0/hWyJNkE+6uWkUc+zOoe5QlVJBMzZ/D/Ln30RjX/TmLPV52MknFqd2JpcIVIYupzdNdLlqv4oIqAI8GEGHyRSGkgR8nahyAwyh7V7ZWgtPH3wysK0KtDIP4VYE8hq/qGQablkETyb0u6++25xCM8wiCbWtdWaVIH+17/+RTNnzqR3332XTjrpJG0T/zFIIrzJEIhCvjLUn5FDi/HrVaCjHQueQesNx80+/vhj//PDSwoSD2+oPlQYbTEejBvLOcGTC2IPcSoQXoR+Qy0ZSto4xxrGCKsGEcZzTps2TXhv4SFGODFwxniw/BHWG8YkAgSuIFwFTzEmAd5++206++yzRY4vSDkIMwxeY4haYZJBhj7j/hAg06pAg8RLQTK0gzcd+GlVoDGxAJPtxEkd/mNtjTD5GngubElk7X5hHX7if2boltZnkPNnjlKoAQE2JbJyZyaTPWUBCCCcOYFJcPnyZQHlRidJbY8jG68briw0Aifd2J+mvfyN79/cwBl00ctDQ1+sagIQOO2aPrRl+Tb69qUpPG8YTxarhfOrrfT62hdZEEv9FAsAK8JJRnYafbnvTfr65R8oJTOFBo84K8IVqloiYOLv4bjuF5Bzzoc8WegTbJJ1co9/dRt/V5tYuFJZeARy8nNo1JT76al+T5A13koel0foJgwe9S+CEJay2ouA8s8f5tcGREluCMk999xzxZqzCDWG51AaSCfUgLGhPQSnQDrhvYSBhMIQggzhJYQ/ow45wBB9gvcXawIjhFiSUnEB/0HfINgIfZah0CCZCPE1Cn1BW5BfGDy08KLCSwmyLsOIUVeTsSCE+vHHHw/YJIGESNQ111wjiCVIq95AiiFwBVzgxQa5h5o2PKowKCzDEKINA/lFLjDCirX503hmhCB/88031LVrV7rwwgv9AmNQlYZ3GWrRIMCYhPjuu+9EuDVIsfReQ9kZIdjw9oJww/AcGBeEruApv+qqqwSuch1gvNbYQNKBIUK+oQKN9njdkMt9LJgpMY1sfa73eS+jeSCXg2wnXxdNy5hqY85qSSDBZGESHJWZKK7ff9jzoT6+jeBqNuJ2LjZga7rGeaPu05WoUz0CSRkp9MCy//LnGzuNsPHvZewhdHrl+3dSTiuVP63HLNz5tc9fQQ9PvZ/6XN6bzrl9AL2x7kVKTg/+DgzXh6oj2rp4Iz1z/Eha+9k0mvfKlzQq51qqKq1ZBE0s4+hOaEFe/ic2ioQWnNhjIndSq1iGqEbP3rp3C3rsu0Z0/rB9NPCGA3TrcwV03n2n1agP1fjII6B+QR0BzGuiAi3DY7XeXxBaEDiIZl100UU0ZMgQQXxBHpFbDI8kvI3SQPy0KtAIFwYxxBJLMmxXttXuQdoQ7gzSB2Vj7KHULA1EO9qxgPiB1Gs3SXa1KtAyL1beA3sQWzwP7qU1eIFhMof4sssuE+cQEJMGpW2QfLQBUZXXaIk82rZp00Z4nkHGEVoOYS9sCD1HCDM8wjDkBKMczyMJt35c8jUTF/AfWS/3KMeY5KYtl9fU1X384AfJHIVIhtdkpZTRC8icEhipUFef+3CP23bB67w+cn3ymizhu7YmkPW0kWTtdkX4djFc6ylYQZk5UlWX2VqAefn/00OZuW5yrlkQUKNOghGAqunw/JG0o9RCe8vNVFhlpn0VZtpVZqa3ho2n4r3Vn73BV6sSPQJ7Nu+lJwc8S399MZ++fOJreuvG9/RN1HkEBPZu2EWv9H2IqlgJuqKwjOzlPgHBl09/IGSofoQuY6ra63JSxZT3qGp/IrkqrcIJDNIrN1eVlesSqPK7dwhtlUVGoHLl5ZSd+RudccluGnjdbjqueylVrLyMJxmq0w0j96JaHGkEFAE+AogbqUCD4H3wwQdiA8lEyC3WCEbIbcuWLYVnGF5JeELh/YT3WKpAgxxDBRoeSwhrIZxXv7wOhJxwHUKhsXwQQqFB9PRkTT6+JL/I+YXnFt5ojAMhvFj26P8zFnkPuYfHFt7phiHUV+HJHTBgAM2fP1+oNyOMHCRUhlTLNXjhxcUYMVZ4c6H0/MADD4jbyPWWsawUDLhqDefAAsJV8MrC641nxLrJCG+G4BYMkwmow16qQCO0+p133hHXAvcPP/wwYK1lYAlPOkK2saQUJh2ee+45cT/cUxJpOR68xgjdxiaFtmRdbd+DzB/s9xat2JpHVQ4rOV3VkxaYXa60W6mwNJFmHbyKzJkqZzXU62lKSKOEUSvJmZjH+f7B4ZCY33I4+cdKxyvIdtboUN2ockagcuHvZHFXUla9UkpOtPP/t4v/f90Ux/vkJDtlZpSRxVUu2inAwiNQyGut4n8c/8tO9gpV8v+3w83nnPrgdrlp1aw14TtQtQEI3NfV9/1UXuQTV1z0wxJaMWNVQBt1Eh6BeR/PFOut6luVMxne9NdafbE61yHg3slCoMJhYiJXmU0QYXtRAmETpJjLeBqfyZub3Ls3665Wp3oEPFUFvFwUr8jg1U4W8Aemh5d9LPxJ31yd1yIEgn9p1aLB1aWhSGIpSRrGHkoFGuQX3sBx48YFPSJCapGbCsKF/F4QNQgjgZgZqUBD5RkGMSzk+GIc0tOoV4H+97//LXKL5VhxnRyLkQo0vKHwJD/yyCNCxKumY0H/RiY9uKiTY9W3Q7gzyCvyd/HsMPwQw/JGcokmnGPi4OmnnxYkWPbRv39/uvrqq8WpDEGG11m7ZBTyemEQAINhHFoVaCMiKssQSg0RLaxVjHxeEHEQYa0KNLz+CIm+9dZbRf8IMUc4PITF9CrQIPfId4aFU8QWDWrhH2eFnWat6Up/Lm1ErRrvpYb1Syg+zkXFZYm0eRfnae/Ooqa9wi97VQsf64gPyWS20LZGj9HWqS9Qi+bbKSuzjCdSeMKECfGOHZm0aWcbOvOW+yj5iI+sbt3QU+H7nzabvawL4KAkchg+gKfc186wUhUKBEDU4jivrcoAKhcvj1TGpENZ9AikZKVQZanPY4mrkPurMIweP7Qs5agD7Xqr8mp8h8MjrCw8Al4Hh4pXz1NzY5DdgAJfB5j4qvJN1ITvMbZrvS52rphZxJLXTg4wj4NxDXS8BNSrk6OOgCLAh+klAGGFRaMCjZxbI0MfEGAC4QP5RPgwllACaQqlAo16bNLQBwhuKBVohEMjTFgaxgISjLV2jVSg0RdUiiH4hC3asUSrAo1JAhBZvYkvM1aCxl4ang3LP2kN3ll4Vzdt2kRPPvmkEB2Dxx3iVxg3VJ4R/o1JBXjFocSMUGyoPDdu3NhQBRrPDNIPQx6yNKkCjXqQeCg9g0DDI4xxohwmVaBB4CW+CMWGtxom24kT/qMVTqtLKtBy/PVbNWLhBzcVlyfSwnXNidbJGt/exEsBNOiUF1iozgwRyOrQkuYVNaUtm+sZ1qc2yTEsV4XVCNjy2pB91SLMalUX6o94+SNbflt9qTrXIdCgVW7AZ7C22l5up/xuedoidRwBgZ7ndqMf3vjF36qiuII69fNFG/kL1UFYBFqc2JYWf/UnawYGTmxVHiynZsf7vmPDdhDjlZYc/o6OJrSZNTssuXkxjlbkxzcntuA5BN/v/8DWTrKkVP9+DKxTZ7UBAUWAD/OroCUzIJZKBboa4L+jAg3COmPGDJGzC6IMkgvVZ5BXafDMom+QeJQjvxfCVxAJQ3jzoEGDhIAVyDsEtOQyUHh9QGS1KtCYgIA3ffHixaL7bdu2/b9UoBEeDfEyLN10rKlAS/xTG9Sj5vyjZP0viw05B8Q2elzTTzZX+zAINOrVgbKZdGxfEOzJOP3ZYWRNiFYoK8xNjvGqlH4XUOlPkzDTFPpJOeEtpd+/QterGoFAfFI8Xfjg+TThno8DELHaLHQyCzm1OkERjgBgwpwsnzSTDv40jbKTvVTEqYHxVi+1zjHTuD4j6PYlY8nMqtDKIiPQ88rT6Ld3ptLOFVsCGg9+4irKylMThAGgGJyY0zLJ2rILOVfM4dpQk4QstNiqK5lTjSdiDbqN2SKTJYXi858j+/qbGQM4tzhfiTVPrNmXMQHuErO41IUHN3ZF1oWR19IxSgVo7JUK9N9TgUaOLHJ6Eb6MJY3g9UVo+Z133inyZxEaDsPav8iflorSWGcYBtyh/ixzdlF24oknijWR4ZmW+b3IzYV6tFYFGgrQkvziOpBvpQINJEIbvN8Dm06kOIuLPd784X/IIDZk5dzLy09fQFlxnCOjLCICjg0rqJv7G2qYcZDMwM/spoQ4B3VutoNSJ95O7qJ9EfuI9Qb2RdPJJPKxQv24E79PyL54ZqxDFdXzn3PLaXRedzsl29xk4/9nm8VDndP30g2PnxPV9aoR0bZ5q2nKnWOoir2UTROrqHNWFbVNt5PZXknl+4pp/Nn3k9uhC6FUwBkiYOaIopOPT6XmGU5Ks7koM9FFrVNKqEUzDkNVFhUCaTc9y6s32Azpr+9T00vpI8ZE1ZdqxKkMad0pseM3RFlXstbJBRTf8hWKb3q3gqaWI6AI8BF4gZQKtE8NOloVaHhmIe6FsOYhQ4YQcpfhyZVLREFkCoa8XnhWQXxBhLHOMgz5tBChgpgWyNnw4cPFmsMQ90J+MPKIYQipRt60VIE+/fTTqVevXiI3W4YiQ2laqUALuEL+cS34iqyVe+iOC2ZRv25rqXnuAWqYeZC6tNxBV/WbT01yiqnq47tChlKG7DjGKjxVlbTnngvEU/fI304Du6yi/h3X0pkd1lHzrCKeXGYV3jcfijFUava4HiYUZT98yJ8XrCLPOcA+D0fg3syTClazi8q+GcfiJfaa3SDGWkMFdknfk6mdZSMN77yGhndZR3d1W03983bT6qsuJcfuXTGGSM0fF8R28g0vcj5g9eRgQC/8HVW4eRctmzgzoFidGCMw/4FXaNv3M6mhuZjapxykNokHKcvmoLkjn6WdM+YaX6RKAxCwNsij1Tm3COFKu5PT5vitiQ3HJRUJtOek0QRPsbLoEfjq2wV03MnvUL02r9L1w/8X/YWq5VFDQIVAHwHow6lA4/YgaRCYmjt3blQq0CCDWhXo8ePHCxEmkDlpyHedNWuWUIEG4YMKNIghlv0xyrnVqkBDoRlE8KabbhIq0FCT7tSpk6EidTRjkWOSe6hAa4WwZLncI7d2+vTpImwZIlNQd4ZJpWtJpPFMWJoJ6s1QwobXF0tFwfOLsYMEw7AEEsSykC88evRooeCMcghUNWnSBIeibtSoUf5xyXxf5BDjOpj0KEsVaCxJhbWRw6lAY5mn7OxsQdSR+4sNHm70Kw0eaCnGhWepa+b46zPOKXLw+4qY9O4Um/4ZPKUHyLNnPVkatNFXqfNDCDhW8zJRyankKT0oSoCnlb1tfuOJIfvSOSw2yVjz7L2yYAQca5eIzzdQXpBg/mjl5T2gWgypF96YFFdLDrCi8cYVFN+ue3BHqkQgUMbr0ZsTk8hTWcERCRy2q30/cosDP/1ADYcMVWiFQWDnog1CIyFME3LwGrYrv/iNul3TP1yzmK/zOF1UMPnXkDisGTuJGvXtFbJeVfgQ8PDvkFVM2JZVdqLstDJKS/I5FYrLE+hAaTJZ1/1KHW8fwmrbKiw/mvfM4kVL6eorb/Q3/eLzr6l7j65018jb/GXqoPYhoAjwYXpNpLCRUoGuVqQOBa2W/IL8Y9MalnxCXi82qQCtrUdeLgyiUhC3evHFF2nEiBGiDOQeoldyqSQUwkOMe0BwC8sjyftjkkBrshxEGpve/ikVaOQ1I9QahvFLUq6/f2099xZH4QXi5/IW7yFSBDjky+g+sCfyuosWqwiDtuZU58CH7DAGKzwlhUx4qycNQHZNlsDPFwmLl/OAPcUH5KnaGyAgPLxgvgbmdTjIvm2rQY0q0iJQtrcoIgFG+7LdhdrL1LEBAlUHDpIl3kYee6AAlmxasXOvPFT7MAhUFpaKnHOv10R7i1PFpm2O3yFVRaWUVD9DW6yOQyAwb95CsSKIdGTg9+asmX8oAhwCr9pSrAjwYXolEE4LUyrQvwsc/j8q0Oedd57w2r7wwgsiF1h0yH/gbUaOLkKVpSG8HIQRHmCETuODJycnx78uLz6QQJJbtGhBGzduFJfBwwyDsjb6lAbP7vPPPy+85JjQwJeAVG5GG+kVRh3I8uFSgb7nnnuEYBfuIYk8juuKmVIhPLI6wnDZ85aWHaFNbFeb6zE+Vl/UQkgk3Lymbb36IatjvQKiLSZeTsqY8gaiY2IlaHOaEnkJRCXwLI4/S0086WJo/Nlpa9DQsEoVViOQlJUWlSctqb4v0qj6SnWkRyA+M12sT6svl+eJueqzUWIRbp+Ymcq/laonCvVt8VsqoZ5aulCPS6jzDh3a+aP40CYxMYHatfel5IW6RpUffQRCfLMd/YHV1REoFehCyszMpB9++EEIV2lfx39CBRreX6y/izDkd999l+bPn0+jR48WJLhjx47Cg4ywaJBbhEwjnPurr76ipRzah7WPQTgRTg5DX/v27RNh30Z91a/v+3L99ttv6fbbbxfLQkF5GksmgfAjXBuGditXrhSEPBoVaKhaS5MkW57XhX1czwvJvYWXnakK9prL8ZsS+Edgrgp/lngY7eM7nOAP3zWqR+xufPseHP5cHT5v2C6GC22tO7EHOIz6swYb/AC0teykKVGHegRSOncla3oauUuK9VVY6J4yzw5cli64kSpp3IO/XxCKEMasCXHU/oI+YVqoKiBgscVR03NPp4LPpwZEekh02t5woTxU+zAIILS59eA+tGriNMPc9FYDe0c1aRPmFjFV1eeU3vTSy0/TPXc/wM6RFDpn4AB6+plHYwqDuviwSgTrML9qSgV6ikC0T58+BNEp7SYJJPKQpXKzzOfVvgzRqkDPnj1bLGuEXF4oRiPnNyMjQ5BfueQQcnRlXi2WRoIHGeQXhiWPkBsMi6avrKws0RbPgRDrxMRE6ty5M1111VXC85ycnCzqkU+N7eabbxbjgrcYRBvt4XHGGI8li+t5CZmz88N63ZKGvseeOfVxE+51NyckUsbwJ1nsKjjvSng0Oboh695Xw3UR83XmpFSyHNebPFG4gK0dTiFTfELMYxYOADNrFRw3boJoYorz/f/yCh+8vEcSHffhZ5TQtFm4y1UdI2CNj6O+j1wdEgsTh5i7qpzUVeX/hsRIW9HzqbsorU1z/pw0cXQCR3EwKbby+/H40bdRk7NP0TZVx2EQ6PvUjZSW5uS5meoPy7g4F9XPKqP+Lw4Lc6WqMkLgtjtupr0HNtPGguU04aOxRk1UWS1DQP0iPQIviFKB/mdUoEGkQSp79uwZ8CqCfEvBLJBQGDy2UHPGBvINMzMhwzJIsGj6AjGHITRaazL/W5bJerlHOUKz5aYtl9fU5T08kkV7m3Kem4k8bs71PfR9Ckccyop21ye3VSlKRvMaJ/YaQIvWNSKHy8KbWWxO3peyMufe9jeROUWFSYbDEfm/uxdsJTdjJt+H+vZIEXY4LKId/ieVhUfAYrVTh0s3UeMeuymnYyE17LaPWvVbTQm0KvyFqtaPQJcr+9Gg13yCOBYmxNISMlIop30e3bPpI0GUZbnah0YAE6ln/zCWTp/wHHUcOYQ6jxpKZ0x8mVpfe37oi1RNAAL43Kt8ojNd1H8OnX7iajqOxSvbtthJp/RcS4P7LaHKdy4x9LAHdKJOghBIS0vlSQUVOh4ETC0t4LlcZf80AkoFOhDhw6UCDfVkLI0k86/lXeChhWAVCKtcOgleYKnmLBWYkeeC0GVYNH39UyrQctx1de8q3EeVa1ZQRVV9siU5yZbIKsUsPOSyW8leFs/Lwtvo4PefU86w++rqIx6xcW+aPJ3KXOn056pESkm0i3WUKx1xvFxFHMXt4WUW7txHyY04V1iZIQIV69aRu7KK9hVnUFpqOSUlOQ4pQPMEFOtAY+qqoiKeSkoTyZJWQpW81FoSi+kpC42A8/OzWDDHS/XyA1Mc3NNHkDmvP5lsvtSP0D2oGiDQ+YozqHmfjrTm+79o2js/U8fT2lO7gT2pxRndyBKnforV9F2S2aMTPTJsErXr3YruukH9D9cEP8/2ZbwEXDlf4qX8pvvFpr3evX05eXatJEtjlSKixSXS8do56+jgriLqdZFSIo+EVW2oV5+6h/lV+Prrr/09QiRp8+bNBAIMYSasbSsNM3Djxo2Tp/49Qmoh2gRS99tvv4llkbAmLdSQzz//fL9o08CBA+mVV16h6667TlyL+2qXQYKX8f777xf1WNZozJgxYj3dyy+/XOSm+m/IBxgL8mGRtwpbuHAhwYuKcOEbb7xRhDEPHTr0b49FdKr5I9WWNUUBh9GqQIPkSlKr7QBLD8EOHDjgD0HGmsGXXHJJwPJDaCNV+6Lpy0gFGs9yyimnCI8zcpFhaAevP0KiEZ4Na9++PZ177rk0efJkMS6sUSwNrxNCsGEYc25urqyqE3vH9i38IzievLyOraPCJrbAgbuoas3ywCJ1ZojAvgWcO87rhnKcAZVWBobnItyvcNVGRYANkfMV2nfu5LADn7hLCS/nUVqWSLY4N0d7sOKzhz3qTit/3lVHcNh37lAEOAyeoqqcMTUyE4uNHVhJpobqx54RPEZl6U1zaOqsLTRzaRH9unQ2TX9tuFEzVRYFAje2G0W7N++jzcu3UdsTWtI5Q0+P4irVBAh49q7nr5gwAaBeNy9buE4R4Bq8XSp5KbPHz3hcXDGs0kGnXK3C8WsA31FpqgjwYYI9Pz9fkJeXXnrJ3yNILNaAPfvsswWRlPmuUC2GxxGETBq8kfAw/vrrr4Ls/uc//6G+ffvS77//LkgTFIsHDBhAa9asEWvJou71118X9WgjLS8vT/SD/ho2bEjDhw8njGnSpEkE8gtii3BfaRgL+oSYEwS8vvzyS0L+bI8ePcSGMpDpdu3aCdEnELhoxhKNCjTGgH71IcRQgcYmDfd88803hZLzyy+/LIgl6iAYhaWNYFi2CEsc4XVAmDMM/SIvGGsC4zpMIGAyYfXq1QIbPJdUgUZf8BJXVFSIa++9916xxzrDMPQlBaqwrjJyehFmDQEteJwhsCWfQ44LkxY7+Qc5XleIb2nHJTo99OfEE08U7xOcQiisrplJKBdXkwqj8atcSyNUgsts6exN4/elcfwuv18TlABWMGrVJWZW39Sa12smuyP0Dz1LQmB77bXq+BACFsbI5VsnNAATN0d6xGcEFKmTyAicfmkvmjnxL0qvr0IlI6MVusXAm/vSuPsnigbHn9kxdENVE4yA1cY5YGF+/kOHwsJtlEWNQGJqor9t9/O6+4/VQe1FIMx/QO0ddG0cGTy9egMhwvI88KyCYEoRJZBfeB7/97//BVwCj2GXLl2EgjLIKsgcTJIweGP1Bi9xYaFPeRl1BQUFBNVjkFgINYGYzZw5Uygkw0MMUShJ8tAeYwERhDcZZBSeS4hDPfXUU8J7umrVKuEdhgcTFu1YIqlAoy/gg3vJMGSUaQ3PAIxKSkpEsQxdlm1APCFoBbIKUo/+kBMsPeFQZcbzgvzCgLkcv+wDZB6GvtD2rLPOklUBe/QlQ60xSQHF6D179og2mCzAGLUq0JHGpe0cxFwaFKbrmsW3asekLUwuZZyNUvr0q2uPdVTG2+SME2jNuMmcOx2Mp+NgKdXvyoqyykIikNymbdS5a14Xf160bhOyL1XhQ8DccjB5Vn/CkzKc1K81ZxlRegttiTqOgIDT7qTM9EQa+eLlVC8nnYp2FFK9xkofIQJshtWX/XswtTuxFdVrkE65zX0rNBg2VIVBCFha9Oay4O8Yf8MKXm+5JdooqwkCH1d9VJPmqu1RRkAR4MP8Arz11lv+HkGu4AEcO3YsPfzwwyLkGWrFMAgwISwZBs8jSCCI6vTp00UZCBQIMEjZ2rVr6YILLhBeThA9eC3z2NN7wgknCM8oSJ0UdhIX8x/cE57Kpk2bilDoa6+9VpBaI9EXjAXkF4aQ3lGjRglSCeL+wAMP+EO3azIWeJrhgdYaSKY0hAmDmCLkVxJLWYc97g3vM8YLDzpCvh977DERlg3yCUN/WN4IpHfChAki3/f9998X5B/edqgto2+EGOtt7ty5NGPGDOrUyZfjgr5AkBF+fP311/ubT506VXjI0Zf04P/8888ByyA99NBDwhMNrGHRjMt/g2PgwGyzUfYt99Ge5x8Mehp++cjkdlHGwIuC6lRBMALmdX9Rg9Qi2lmkFbvCDxUTdWi4i6qWzaO4k04PvlCVCATiWKk9pXN7Kpk7n+P8woDCTva0np0p7hhTZA/zxH+7yt7kNvIsmES2BA6L9LDIHYeSux0mquw8jnIs1YJOf/sGMXJh8Z5ieqb/E1S6v5QcHCKJvN8qDpu85MnLaNA9g2MEhcPzmPs27aGvH5tEG/9cR3GJcdTnur7U/85BLKAfOtrj8Nz52OjFnJpNB+sPotQtn5LFHEyED+bfSKkp1b/Xjo2n/uefwl7FmhO8BEFCkorU+ufR/v/fQX1a/P8xDOgBa8/KDWGvyPsEicSyOMhtlQavq2yHpZMQ0gyCJ5fSAfmFgdyi7dVXXy1I4amnnkqDBg2i5cuXiz1yi+E1RMizNLQHqUXuL8obNGhAt912G8GbK72Wsq1+j3EiDBd9wKTwE45rMhYQfTyTdpMEEiQ+3DJIuBfyj0F+hwwZQg8++CABI4Qyg7hu2bIFTQRZx/OdccYZ/lxfqD3j2UHk8QzYY9zwwAM3bMAQ/aCdVImG1xge5MaNG/vbIWd769atwqOMvuCBxjNgr10GCRMRMFwLwz0jjUs0PEb+QHl31ROv00EWFgLhxVtRbg6nhfbuT6aSJSoHONLLXfzzd7T/vdeoUVoxtczeR+mJFZQUZ6fM5HJqz+Q30eaknU/9hxw7t0fqKqbrG9zAn3/hfn/go43fpw1uscU0TtE8fNW27bT0yiG0fFI+rfu5KW2fn0MFs3Np5dctaMMjz9DeKT9G003Mtyk/WE535t1Gu9fvpvKicnLyskcgv7DPH5pIU176LuYxihaAMp5AeLjz3bRo8lw6uLOQ9m1kMjx6It3d5GYxsRBtP7Hcbuv0BfTDK1tp3sKW/NuUJ2IgWMlbVVUczVvUkn58ZT1tm7UoliGq0bPjt+rrIyfQeVk306CMG2lQvRvJzpNcymo3AooAH4HXx2gZJBCkiRMn+rcPP/xQED14RiHiBHIMzzC8jSDS27Zto127dtHJJ59M/fr1EwrH8E6CpCHMWi77g8eB13PYsGGCJH/++efiCREKjVBdkLxQBvILMawVK1YI4auWrI4Kz+qiRYv+9liM7gVBKuTOIrxZClZp24HggqiDbCKsWOIkRabkGr84BzEFwcWYcc3zzz8viK0Mm0b9mWeeKSYJpk2bRrjm3//+twgDB/lFDjTsuOOOE3t4udEXiO9zzz1HEDLD5AEMfSJ8HDi98847oi/gPmfOHFEvJwuiGZe44Bj5Uzx/oZiscDjjaF9RKhWXJVFJeSIVlqSIY3gvC954+xh52n/uMQ5MHO/vvF5SJbXO2U/tG+2hFvULKYnJL8zDQmPFU7/xt1MHgQh4XPvJnLyVWr6eRtZ6TISTA+txHt+Ulz97E/mXaznU3KcCH9hKnUkECl7hddL5MxRWujuZ9q2tRwe3pDFuvp8OW//7BkdGh/5Okf3E+v6n137glMrQAXdTXvyeqsoM8qxjHTjd8+N309On+CKN4GmThmMXh5fPePtnWaT2YRBY9NpEUbt5SwP6esoJ9OvMzvQLb19P6UkbNjUkpIfINmG6UVWHEHj2hnfo27enUVWFg1xON1WUVtEdpz0uHCEKpNqLQOhP5No75jo3MqNlkDBjBJEkaQjjhXgSvJL33HOPILFSBRp5qRBQQhguvI0IgQaBhOcXJG38+PGkV4FGyPSsWbP8odDwyIL4GalAYwyS/C5ZskR4jhHCDOKuV4H+O2ORzyj3kZZB+umnn0RTkE0tRvJ6qVaN/GV4wBG2rFVbxgQBJgewDBK8tfD6gtAipBr50jB4fK+66irZpVDrxgkmD2RfOAZ5zsz05WhJgotJB4iFffTRR8LbCxVoTFTIvqMdl7w5Jj8gzAUrLi6ucyrQJStYubjKfuhxTOR0BX+slK9bf6he7YwQwOcB1LQjGoTali+O2CxWG3jsm5mwWciSaKKWr6ZS+XKX2JxFHibEZg6PtlJyRyuZrEzqTFZW3N7EWjDHxypcEZ+7dClHbiCsI4S5+f/evms3JTRpHKKFKgYCK35ZzmHjPsFGI0QQurt12VZqc1Ibo2pVdgiByuJKqmBvupG5GN+5n/5OZ40816halWkQKFxb/V3jYaHAktIkTa3vsHB1QVCZKghGwO320NQJv4vQZ1nr4bJdm/bS6nkbqcOJrWWx2tcyBIJ/qdayAdaV4eRzyDI8hNocYCgU79u3Tyg1a5dBksrL2meDpxEbljVCSC8M4cNQeIbCMfJeIZYEsS14ceUySPB6GqlA4we1fhkkhEIbqUDDg2y0DBKW6tGrQNdkLNrn0x4jRBiGvvLy8gTp1NbD4w1y+cQTT1DPnj21VULpWXrU4S0HOdWrLcMjDAKM/GI8A0gwSDfyoDG5gOdFiLXWECINw+uE1wB9w2OMfpDTC486ymB4fTCZoFWBRki19FBHOy7RGf+Bdx/h6TCpai1O6sofni0WMc9hxuvlLwRloRFApEIkDP1XH1I+95+rAz8CXq/TfwySm9ItTmz+Qt2Btr2uSp0yApG8u3jfRmqjgOR/7Uiff/x97Vb/1xHfKh581+CzMoS57KEnGUJcEpPFWu95KACQ2qQsMgL4rW2LjwsKefZwVAJE75TVXgQUAT5Mrw2IKUKWP/nkk6AeEcZ7yy23+EWU4CE0Mngc4c0FacYySNKgOgxBLYQw33DDDaIYAlLwBMPzi/xYaQUFgSrQ+mWQEOKrV4GG+JN+GSSsR4zlh+ARBjmWKtDRjiXcMkgQrkKoslRuxo8oiH9J4o96GITDjGzxYp8HDB5zTBroVaDhAYbJcG887wsvvCCWmJL9IQcZz4UliGAg3Y888ogIMQdhhhAYPMgIA//ss88E8ZbLICGXW68CjdAseb9oxyXHghxxaVimqa5ZUuuWZElLJXdJacihJzb1hZqHbKAqKC47N7IXmCNEEo7roNAKgYA5rhHXhP6BHHiZl4WI1PsyEJPAs6SWPLG7aElgoe4svlGg2KGuWp0yAi17taJty7eGJMIQxWrcTr0XI71ZUnjpqJSsVKoq8eVPa9vDi972NN9qFdpydRyMQFrzBhTJw5vWXP1fByMXXGK1WuiEszvTH99wKhiTXmllnOvfjv/vldVeBBQBPsyvDTym0kCKkLcLRWYQMOSZRqMCjSWEjJZBAimTdvfdd4tD/TJIsl6qQOuXQZL12j0IqHYZJChQQ5AL44cAlXZpIHhXYZHGEm4ZJJBHkHzkF7/77rs0f/58Gj16tPC+Ive5VSvfh8bgwYOpQ4fqH/vIRUaosVZtGeHQGKdeBRpjlMsS4dlmzpwpvMkg3SC6CGGGejMmHCBeBU8xNnhvn376aZo3b54ox9rL8Opv2rRJjA/9IvQcYlsYHyYO0B8Iv7wfPMbRjAt9HQtWr/eJ5KhwEsQkQ03O1ztn4LHwqP/oM2QMvpj2/e9N8nKeb0jj93r6AKUYGwofS3wehzTX49Dm0JMx8lpzXC6ZbSp0V+JhtG9y41BaNfwOoypR1uCKS8nMa60rC49A/+EDaNa4GSEbdTmnK6Vlp4WsVxU+BPBb5fJn+9Ibl/lyWP248DJ88LJf8PgF/iJ1EBqBjjcMpj9Hv0vO8tB552ijLDoEHv7kdrog91aR/xufZKPElAR64/dHKT7BFl0HqtVRQUCJYB1m2OGNlVv37t1rrAJdr149MSKQUJhcegjhz6hDDjA8pfD+Dh06VITMSlIqLuA/+JKQKtAyFBreZazti3O9oa1cBimOf8ykp6cLbybIOnJgpdVkLAi1fvzxxwM2EMPZs2fTn3/+KfJs0T/uh9xmhB5Lcas8DouGaRWZodwMogxDzi0MecvYIGaFvhBaPWLECLEsEvBBv/DiwmuNJaEgrtWrVy/xTCCwqMMEAmzjxo3Cey/VqSXJFpX8B15huY4znkOrAo1cYuAqFbyjGZfs91jYz3vlM1q3N0WQX80EqHg0nO8sT6GZ//2RKg8UHwuP+489Q/oFV9L+Io5c4KVmQllR3qmU0Mon2BaqTayXJ+SMjAqCxNz7omoXy40yevei4qY9BATyfxtfIS5+j+6rTKIGvDqBssgINGzTiB6f95RoGJdQPWGQlJFM3c49nm77eETkTlQLcnOOf/P852nkxzupXkMX2RI9YsvrbKeHvt9Bnr03cEi+L1VJwRUagVZndKJ02m/YgJMaqEn6QWpxqvqeMQTIoBBE94ficTRmzmh69vv76MM1L1JO0yyDlqqoNiGgCPAReDVkzirWBI5kMow2X7cMUteuXamoqIguuugiGsJLAyFXFCrJII76ZZBAdrUq0NEugySFsEAUQTKxh1KzNBBtkOtoxgJCql0CCccYLzywEPzS5/aCMEsla5BNTB5AxEvaa6+9JggryLq8FmOByT2OQUTlhnJ4hxFKDY86QqvhYYbJiQY8MwweYXh6kZ+NZ5aq0CDPILZ5TMpBmGHa++FcvmY4hsl6uUeZHBP22nLU1WVzllfSyo+mkstroVWF2XSAfxRXuSzkcLOwhsNGW0sz6EBVsphpXjNpWl1+1H987Ct/WERTd3ag3eXJAj+nG+9fIjsr7mJbsCuXfp5lp9I9Srk43IthTepC9rhHRRN9WqWLU7LKS8zkjH+MLAltwnWj6hiBNZN/ozXzdtGyfTm0syyV/78TaE9FMq0vyqKC8mz6bfQHCqcoEUCI8ysb/ktNOucRz1CzWCDR1a9cS3d+7ovmirKbmG5WtfM58fyNWjvpP1/voEd+3E6P/bKNho/dQ+k5Lia/ZeQo+jKmMYrm4Xe/9TId1+AAtcjcT/FWJ1lMHrHhOD/zAOXVL6Y9774eTVeqjQaBZqatlF+2UHl+NZjU5kMVAn0EXh0jFWgQsw8++EDcHaQIAloQUsIeeaf6ZZCQd6pXgf7uu+/EMkgI4wV5hLKxNL0KtAyFBgE1ImCS/EoVaHhKse4uQouRH9upUyf/kkw1HYscE/bIfwb5BEnXGryqCD8GyQQBBmFFji6WG8JkAMSoEhIS6JJLLhHnuBZjhgf51VdfFcspZWdn0/vvvy8IKUgp+gLZhsdcetTRF0x6zWX+NPDB8kzI44baM/rGa4HjO+64Q/RTwPnVMBBpjAuTEfCKQ8UZxFxaNOPCM0pbuHCh6BPnuLYu2d5lG1l01/fsblaT3F2ZKjb9M7gq7bR1xkLqduuF+ip1fgiBVVMWkLOCIxYK8inNZqdcXv/XZnFTOS8vtasshexuKyWkE22avYq6XFj9v64ADEZgwS9u+u7VxnTmZUXUqY+dktM8VF5spqW/x9O0z7Powvs81P/64OtUSSACayf/IQoqXXG0o6zac+lr5aINP86lM1+4NfAidRYSgdkTZ9P6BZt5vVqfwNC4EROox/k9yaZCJUNiJiu8niry2DfKU/4dw5FZSRyOoDVvFblKZlJ8/Wu1pepYh0D5/DlCdLFReik1TCsljxd+X14Fg0PJgSvx21O00V2nTkMjUDF9EhWPuVc0sK+YTfXuUhMIodGqHTWKAB+m10F6AaNRgQb5BekdN25c0N0hPvXss88GLIMEYSQoHRupQEOxGCaXQcI4pLdRrwItl0GSY8V1cixGKtAQ4oInGTmuEPECIazJWNC/3kByEWINgwo0NphcD1gqN0OcCuJWY8aMEUJXaAOvNJZlkoa+4F2Hh1YuXQRP7bnnnisEvGRfaA9vNqxFixaEiQOIlSG8G95sGJaVguo0yPTIkb4QSkwU3HnnnSLXF21wP9iFF17oXwYJeb8Iz8YEhFYFWj8uhJgbjQv9IQ96xowZODScnBAVtfSPo6SM30S6HyEhxlpZWBKiRhUDgbL91SHiJY549qBXT5JIhNysKlkZYhkQ2UbtWVl98Sbaspo/Y0dnBMFhIjftWFbA5acF1amCQATK93JMfhgLl0MY5rKYq/K6Ssmxcyxt+eM3Jr+p/ucvO1BGhfOuofpd7iRruprU8gNjcOB147dC5J+svnYGHagiPwIehy+aDQUgvCC+evPY69ZkvH78R/q8fMr7/ltW/f4NkSLAfjxq60G126q2jrCOjEt6NEGs5DZp0iRBbOC51KpAa72F2sdDH8uWLfOvfSuXQUIbrQo0PLPIUUWeK5ZAwgZVZRj6AMFFPm3Dhg0JKtArVqwgjAWh0Ni0HmCMBSRYqkCDFOJaEDKQT/SFsUPwSS63FM1YIAoF4SjtBu8qFJKxB7lFnyCOWD9XroOrJefwCmvHihzg5cuXi+fEH/QFDzAmBxAGDjKLvuFBh2n7Ou+888T4EVYNrBA6jjFoDd5pLCslDRMJCL3GRAJMqkCjX7mUExS04RFGW3k/2Q6vu3yt4T3G2GCynTjhPxBImzNnjtjgxa5Lltwgi0xmTBlHttQmOZEbxXCLzGxERQT/ENFCYvHwBFJ2grZIHesQ2LVwDW3/dgZZOazPyMz8Y2/9+G9p77INRtWqTINAZpvwysRJ2cETDJrL1SEjgJzUypVXkGvPJOp++gE/JmYLe9v4F1hqwlqyrx1Ozt2f+uvUQTACJha3E67J4KqAEnOcUi8OAMTgxJLqc0IYVPmLLGmR2/gbqwPKGFnt8U275WmFSB1AIPJ0Wh14iNo0RKUCXSjW5g2lAg1SG41CMtqAeILMgkRDmRnLEUHRGSHHIProC+QenlmQYIRvQzgLnmqYVGWW7w+0mTjRpx4JsSspWiXrEc6MCYIhnGMNQo2cbXjAcR+Y3NdEBRpjRcg3QrOlyJd+XJJM4x6SMOO4Llj9Di0iULbqp2gxUHk4qtEIPHJvWURtWURjWVxXsnPIcyhze8yUO3sEefvNIFOSIh96nPYsXU9fnvdvymTuG2dOJpcbEwraCRqeqOJwv5wEF31+7n106ZQXKbuTb8JM35c6J+p09QBa88WsgOU9tLh0vqa/9lQd6xAA+a1YeDKX4j3opU4n2ume1zbTl281oHP1iSEAAEAASURBVGZtKumqu3f5r3Bse5VnsFN4OTSlvusHRXNgMlnJmtKbQ5xncKlbU6M9NFNchlpxQIuI0XH6mQOp8MtPyKvxBAe0Y+dK+pmDAorUSXgE3PWaUuFt35Cn+ACl9DgxfGNVWysQUB7gw/wySAVo7JUKdLAKNHJcsYVTbsZL8tFHH4nlkEBmkY8MkSws/QRPK0gvDIQUYcdXsxIp1KWlcjOWWAKpRM40DLnAuB/IryyTXlrR4NAfeMG7dOlCgwYNEkQaBBnHuD9MqUAfAkqzO1iwm8oP+MLYNcUBh/ySCZJctvdgQLk6qUagcsJt1CznILVuso8sZuMfd2b2aF522mKyVu0h+48vVV+sjvwITL/bNwtv5W+2k7ORsmBiTzCv98tbHOOXbPXQmQ1K+RyeOQ/N+Peb/mvVQTAC28aMpdx4mbpQHZ2AjMF0WxWVffMFucorgi9UJQIB564P+S1o4+Nq7Dr2KqdHP9hI1z+wk3N/q8vJayfHtpeEkJOCzxiB+Aaslm1GBIzBT1dTHFe1YgJ8tvHFqtSPQPaQYWROyzDMXhIZTawemHPjHf726iA8Amv+Wk/XNb+LnrjsHXrypi/osqxhNPmVH8NfpGqPOgIGnyJHfUzH3ACUCnRfoQgNFWgZ0iz3eLFlzjL2KIdHF0JbZ599tlgvWCo3I3/3008/FSQV10H0C+QW4cxSuRl9YGkjLBcl7wHPMUKskVeszdFGH9IQ1o08YeQCw/RhyijDvWCyX3HCf/RtZb3co53+GeW1dX2/8K3J5DVbeVkU35OA7GpNkl8oGs//75dUoclz1baL5WPX+jnkKdkrIDiv90rq3GInWVn8Kj6O1TmZDCfYHJQY76B/nbScmuXyJIKTfyj/xbP3vFdWjQC8v2W7qkNM4/jb7ayGpdQts5I6pFfxvor6ZFewZ7j6muKCXbR/VUF1gTryI3Bg/lIqXLicBdmc1DS5hNLiHJRgcfIkgoMaJJZTbmIF2fcXUsHHX/mvUQfVCOAz37l3En/4O6oLIx15HOQq/CVSq5itN1szKaXNt+Qx51F5qVlsZQfNVMHH5pQzKCn/3ZjFpiYP7mHByjkLbVRut4klzbCsmYu/o7GVVCXQalc3cjtYplxZRAS2rt5Bd/d+jEo5n79kf5nY46J37/6Yvhuj/pcjAngUG6gQ6CMAvlKBrgY5GoVk5NTCpJf2p59+EgrU8KgjH1mGLkMwDIQVy0NB8Ap9I/cYdtttt4k98otBmuG97dGjh1/9GbnHuK5Vq1bCU79582ZBZBEGjbxp5BojHBme6CuuuEKEXEMRGna4VaAxbixrBUM+dl2yLTMWC0+al2fknRxuivxKyS/AhTGbDIVJeOKQK7zjr5XU+lwVCq19jV3rZxNVSi8b0TknrKE+HTdTwZ56ZHfwutwpldQ8p4hscdWeYRMnD7q3LyNrfk9tVzF9vPPPlbzcVmUABkhPrx9fjVtAJZ+4qhz8nlxB9dvn6ati/nzXlGnkPfS5lGh1ETa9uSuraMfXP1Orm6/WV8X8ubdyI2MQ+r1nCJCngtwHf+cw6AsMq1UhJpwtdHl7N+U2bUhNW/GEgdNEW9cnUPdzcunu9y0KoigQ2PnHEopLTaFVLHkSz//XSTzZCqvg9Bu7y0q2dDvtmrOMmvX3Rb9F0WXMNvno0S/ZKQInRzAEE5/+hgYO68e/H+WvouA2quToIaBelcOMPdSY5QbihdxVeCfhpTznnHP8d8PsMFSgscn80G3bthFI3euvvy4IF7ygUnkZXs0BAwYI4St0MnDgQIKwUl5enugT99QavI/IoUWu7DPPPCOIFVSgca43jEWvAg1xqUcffVSITD3++OMEEvp3x6K9n1RuBh5Qbr744ovFc0AhGQYiixBmGIS7YAhLxvq8eEYQW0kWUQfFbNkXRLogNIV1f6WYFDDEPdEvcoBBaGGYlMC5VF9G+DNs48aNQq25ffv2AmssdwSVZhj6gUkVaCw1BWwQno3Qar0KtByX0TOKjg79ueuuuwQJB8GXz66tr83HVcWsAn3I2H/P+ZVMhDlPFZuLN8w0g/zCQDYq9qkwaAGG5o+3cBt/ewZOfKQm2alT/m7q0XYbtW68P4D84lIvt/ce8hpruorpw7Kd+8njqhnhgKp2xe7CmMYt1MOXFWw3/lWnu6Bqb7XXXVcV06deF7+vxORfzWDwOnwTwDW7KnZaF+0ppoTkRNq9NY7mT0+mxb8n0YHdZlr0c7VAZuyg8feetHTrbsLkFb6b7bzEWVFlkthwjDJXRRWVbq3OT/97d4mNq7as2G5IfvH0VWV2qiwNnJSNDVTqxlMqD/Bhep3y8/MFyXrppercPJBNEDGE8mL5HoQAw0DmoDiMNW2lwfMHD+Ovv/4qiBmWIJIq0JMnTxYq0CDAa9asEZ5K1IEoSxVo2Q8IMfpBf1IFGmMCmcTauiBrWAtYGsaCPqUK9JdffinEmuAtxQZRL5BpLOMD1eZoxwJPLLyvWkOOLry6ILAgnxCZwlixzvD3338vmiKcuKLCl1MGhWXggTVzcfzxxx8LAou2WBIKhjxg9AVy/tRTT4nnwDNKg/ozNhjGtHjxYrGs0wMPPBAwIYExYLmnk08+WbxOWO4J4lvADiHUV155pd8j3bt3bxoxYoRQsIYHGmPAOsIyFDqaZ5Tjw37IkCHiPYJjTJjUJYtLjOcvy+hCcc1xPLOc6vsfqEvP+E+P1ZSWW/Nb8P+kKTmz5tcdw1ckZrNqKeMS8teIwbNjDetEpWRsgAxH4GSkGZbrC61MRpQZIGCFiq6BW8igaUCRuC6gRJ1oEEjkyUGPOzisPC7eyf/6Lv4IUD9rNXAZHsZnpJIlIZ48Zcb5+5Z4G9nSUgyvVYWBCGTkptO21TsDCw+d4Xd4Upr6fDQEpxYUqk+Kw/QiIIRWbyBEu3fvFqrHIJhSRAnkF4QOnmGtIbQX3k4oKIOsglTDpkyZIvbaNXBFAf+BZxhEMzPT92MYXmGE9kJx+NJLLxXEbObMmcKLiZxZhPhKgok+MBYQXKgrg+QiXxlLMYFMNmvWjFatWiW8w/CIwqIdSzgV6KVLl4oliEDEgVFiYqLI50X/UEiWastYkgnkF/bYY48Jwomln5DPKwmwqOQ/wG7t2rX+EGhZjj2eF0sNgUxLA2mGOjPygmGNGjUSG44xJpBhkHOQZ+QNgwzDowtDPy+++KJ/ySRMFICAS3VnjD/SM4qODv2RY8ApXoe6ZLldW1PBtIVRDRkh0A26tY6qbSw1sjTtRJSQytPF4cXEAjBxOcjSqF1AUayf1G+XJyZYHCW+SI1o8IhLSaSsts2iaRpzbXJOO5H2/T6PXGWh8TRxmkj93t1jDptoHtgcz+8rry+0NJr2vjZWFoLuEn3zGGvpce4n74Hb6IaHnPTWgxni6c1m33JSD4/bTGUbLqCUlhySaobwmLJQCDTq0zXsRKGTiXGjU7iNsogI9L/+VNqwYDNVlsGjHmjHD+hU51b2CHyCY/tMEeDD/PpqRZYgrAQv59ixY0VYLsKdQSphyC/FOrswhO3COwmiOn36dFEGAgUCDMIKYodw29LSUuG9BQnMY08vhJ7efPNNQUqxrI/WcE94KuF1RSj0tddeK0gtwp31hrGA/MKgnjxq1ChBULEUkdZTWpOxwAsLD7TWQAyxIYcWpHfChAkBSwTBQ45QYuAGkxMGX331lSC/KAMhxvNrDaHJL7/8svDAG4V4g1QC2zvuuIM6dOggljZKS0ujhx56iMaPHy8UnxH6jNfguOOOE33NmzdPlMv74L5yPD///DPdfvvtwiOOiQ30A6+2VKGO5hllv3V933XoINoxZzk5K4Nn5AOfzUtZrZtQRotGgcXqjKzt+vLyJ4Hv6fCwmMiS30Mtg6QDqfHJnckM+ecamIWjEhr16lCDK2KnacNz+tL6MePDEmAvezha3nRl7IBSgyc1WRLJknYC5/T+xld5orvSkkDWzDOjaxtjrTwcUl628SLx1H0v5EnrfDv9/CkvGxXvpQtvKaH6DdysR2Gnim33UVKzl3liPzjdK8YgC/m48WnJZK5E+pIxRvFmF/9G5WgaZREROPPaU2jjogL65rWp/gAkeH3rN82kh768K+L1qsHRQ6BmvxaO3jjrzJ2hWCw3hNUitxUkEkJHyEeVBq+rbIclkxDSDC+nFHjSen/RFkv9gJCeeuqpYmkeiDRhiR54JbEurVY8Ce1BamXuL9a2Re4svLl79uyRQzDcY5wINUYfMHhBpcH7G+1YQPTxTNoNBBcEG2M944wzxGQAzkEmMV4co388E9b/hVBVAXu0QfJBXGEgu506scfskMGLDPKPZZBkiLmswx7KzfDYYjIAHvH0dISlEfXv31/UwYMOQ+g3cnEheKVdUgnX4jXJ4wkHkGDcA3v0BRKPnG1MRMCwBjEsmmcUDY+BP4275VO9uFLOGor0A89Ex5/WyP++OgYe/bA9gom9v/H972QvcLQhZ15KOH/0Ybv/sdIRwvF7jLiUJ8iCJ/lCPWOve68kSzzy3pTpEUCI5MmT3hLFJtaECDD+vEZ9n8nvsseteUCVOqlGwNbkdrG2b3VJ+CNrRl8yJ7YI3yhGa+373ucnr54obNvNQXc8X0jDniiinCYy95/X+bZv4EmbOTGKUuTHhhNk9q2jycafkwlMdLGkmS9UHyoeXgL5tcXxqg33vcAh5dF/lka+87Hb4pZXr6GnfrmfLrx3EA26tR/dMXYojVny9LH7wMfIkykCfAReSKNlkEACsS6t3D788EN68MEHBcGDdxLkGJ5heBtBpCGQtWvXLpGj2q9fP0Hepk6dKvJYEWYNsigNXtBhw4YJJePPP/9cFCNHFaG6Mk9VttXuQX4hhoV1diHuBBIKL+2iRYv+9li0/eMYQlEguSDTuA8I+fPPPy8IMMKIYai//vrrRRgxyDFCt0HiYfCyIk9X2o8//iiUoBFODtz0BpwhiqUPH5cTDXhmGPDBfeHJxXrDeUx4MR54eG+44QZBdnGOcGpcA3EsPAtwnzPH92UrJwuieUb9OOvq+cHffqc2uaWUmVip+SKtfhqsXWvjL9RuubuofNYv1RXqKACB+P53kKf5aQFlRidut5kSbv+KLE07G1XHfFn7szpQgwzjvDYtOPih17pRMbU9U3l/tbjoj22ZGTRgwRRy1G9CDha1Y6F3crGwU4U1jXpPfJPS27fRX6LONQiYk1pRfKuXNCUhDs28RGA8a2S0eDxEg9guBhFzlc5iEIKVyIOQ8ZSRs/jHoGJV4EPAfuAgFS1bK0Kg4yxeXtbMFbDZuIy9FHRgyWpyFBYr2KJEoNuZHWno81fQzUyGT7vsRA7qMvauR9mdanYEEKieTjsCN4vVWxgtg4QPdOShSoM3EeJJ8ITec889Qq0Z3kmQrrPOOkuIRCE8GN5GhABDfAme3+eee06E8UIhGURRGkKmZ82aJcKv4f2ERxYq0Mgt1nqLZXtJfpcsWSI8xwhhBnEHcQQZHjp06N8ei7wH9sg5hoc3NzdXqECjDPnFIPUg9PDYwsMKESoQ3T///FMoNYOcwpDLCw+rNHhtteeyXO6BK7zNesNzwuB9hwHP0aNHi3zfJ554QpThnpgQkCJakuBCzRseYyhK496nnHKKmKiQol/RPqO4Cf/BMy1cuFCcog9gU1esctNG8lRWUtusSip1lNGesmTex/MPZRPF81q2WbxWaE5yOVk5T8tVfFAsq2LShbDXlWf9p8dZmDeU9kxZTa2abeHlpIgV2H1eDf4tQk6XhVMDrLR8Q3s6PVflZoV6LZw7tlLn/GJKNJXT5gNZ7MHwKZPL9haekMFSXS2yDlBL9ho5thdQXMOmslrtDRAw2eJp9Srkp1eLriWk8/95qZt88TQGF6kiPwLW9J5kav8x2Tfez59/xeRxlvD3vK/a4+XvfKuNw54HkC3vARUh40dNd+Ap5Skr6eXV1RmcehxbDUpVERAoWVfAOdKH3oB8jp9Wvl9XgfiYmcAVc9uc3ur7JhCZ0Gd9TVeJyqlVH5BNRRaFBqqW1CgCfJheiPz8fEEQtTnA8CZiWRsoNYP0yWWQpPKy9taVTCKwXXfddX7ChvBhXIvcX4guQfhJrleLZZCQ2woPKtpIg+cSRA0EG7m/6A+h0Mg3hhfVSAUaHmT9MkhoB2EovQp0TcYix6TdI4QZgl16FWh4hEGAsVwR7guCCu/qk08+KQg7wr+xrJSeHEryi3Hh2fU5wCD7wFUahK/gcYa3/fTTTxc5v7IORBlkFkrRCAMHtpL8oo1cBgntMJmA8UkV6Llz5wYsgxTNM8r7IrcYzwyDInedMo42kJZqc1BqpkOeBu1FOBW+bZWFRGDj9ua0YX09ys0upnoZ5ZTAyqYVlTbatz+N9h1I47Ubk0NeqyoYAfywc1VSy/pOappxkPaWpdKB8iSxtiXWu8xK5gmZ1FKyWXhWgZf8MJmr378KP2MEkOcbn8YYllR71u2lFWJdb+MrVKkeAUtKB0rsPJmc+/+gGWNf4RDTQiovs9DxZ59PuV0u4XByX/qM/jp1LhHA90akNBvZFvtqgqctVceAhrGJ8msYKvnKokegWbtGtJUVoRX5jR6zo9lSEeDDhD6IKUKWP/nkk6AeoQ58yy23+HNU4SE0MpA3iDKBNGMZJGnIQ4UwFEKYEY4Lg+gTPMHw/EovJsoLdCrQw4cPF0v5yGWQjFSgoUitXwYJua1Y8ghEFORYqkBHO5ZwyyCBkN57771C0EuqQMMDDMM5xgPii3Bn5E9rDR5irQgWBLVAaKWiNLy2EBIDSYXheeG9NjIIY2EsmFyAwQsL7y9IOAzeeIQ3S886PPQw5HLrVaBBtGV4OdpFekbR0aE/8jXFKTzfdclSWDzNzDnSnkNrJIcbezyLopk0hDlc21isy+7C4aRuJ4fhW2nHriyx6XGIZ89bfL3gUH99u1g9txT/xF42LMtl5hw3DzXJKBabER5eJytpl0zlqurIGaN2sV6G3Oru1/SjOWO+E1CY+UdxSm4GNe7O71dlUSOw8rMZNPupD3kJn1RatD2ZmrL7fOvEedSwezEN/t8DZFUeo5BYmizQB0lkL3D1ZHbIxlxhTlDvzVD4pLduHtV66VhTPa11XqhuVLkBAuNXvWBQqopqKwKKAB/mVwYeU2kgRcjbhSIzwlyhMByNCnSoZZCgziwNeaow/TJIsl6qQCO3FUTv3Xff9RM52UbuQRq1yyBBgRqCXBg/PKUIwZYml0GKNJZwyyDBm4u+9SrQuAcmC+AdLi4uFmrRF198sRDAAtHHuBAePnLkSOF5RXsQUUwYoC884/z580UoMzzuyKOG1xuecBiu/+mnnyiPPcXw2IMoQ1QMBm89iDTK0ScUu0GIQcDRb+vWrf3LMyH0XKsCjSWTQPi1yyBFekZx02PgT/qJvUUIVcRH4Rnn+ofWY47YNkYbxFdspOzUvbSzIoMRMJ6ib1t/MXn2byFLdl6MohT6sT17F5F52wRev9ZG9v0QbTLG0NeDl2wcrWAq+IDxvJDM9buE7jjGaw7MmEmJ0ydQx4YW2l9ioiQbazGYCqjgldcobySLtymLiMDi976nWQ+/52/XAXp3HNHrYNmLrbOW0Bt5l9CwVR9SQr3q9B5/Y3UgEIhLP4ccRV9w7iomuMKYycafAeeGaRDbVfFZGdSgT3fa8escnizkN6GR8eR/g1N6qMlWI2wMysoLy+irhz6h1dOWk8vupObdW9KlL1xL2S3qTjqbwWMd80UqvuEwv8Twxsqte/fuNVaBRoguDGQNJpceQvgz6pADDO8mvKDwbIK4SVIqLuA/ILRSBVqGQsO7jLV9RRiqbHhoj7ZyGaQ4VvuEUjK8mSDrWItXWk3GghBq5A5rNywPhFxjbDfffLNfBXrEiBFCZArPhDxnrLmLNhgr1jMGGZd4gFiC7MKg1owc4VtvvVX0hbHjepBfXAeDMBbUshG+DMEsHGOSAv0hjFyGOIMYY8kqLJXUqlUrMR7ggDHIEHO5DBKeQ6sCfdVVV4l2WmGtSM8oBncs/LGXU6ptPz8JC2eENJ+6ZEqqM2SLWK/wlB+k8jFDqEenjZSWwoJinKtabR7+f3dRxzZbqUnOHioff091lTryI+Be+BzPZHGKRfcSjjTwF4c4MFHWCcw+mIG4F70Uoo0qLlmylNbdcx+5OcWkvoWXiatXRM2Si8Sk164PP6Ktb72jQIqAwK5F6wLIr76518Ofj+xVnzriNcPvZ337WD2Pz7qSTNZMxig0Am63haypfcmapPJWQ6NE1OO5ewX55bdekEHojj0U1OvVwOi7oIaqQCDgZMJ7X/NbaO5nf9DBnbxU14FSWvnzEnq0y920dfFmhVItRkAR4CPw4hipQIe6rQyjhScSBnILQtu1a1cqKiqiiy66iIYMGSKIL5bkAdnTL4OkV4GOdhkkkDaEO2/ZskXkwmIPdWVpNRlLqGWQ8CwwuccxSKbcUA5vNNb+Rdj2999/Lza5zjFIpvToIuwZIlc9e/ZEN34D+daqYgOf119/XeCG3F14mHE/eHGlYbkqtGvRooUsEpMA8FRLYovwa5h27DiXrxmOYbJe7lEmnw97bTnq6rJVzJ5CSalezlfFmoIw7TcqnpVVoONc1CC3iMq+fFvg4Gun/moRcC7h0F2Pm/Ei6tt7FZ3QZSM1bbifcrKKqWXzvXRy97VizwCSq2AJeYp2aS+P+WOvx0Xe3X8KHOLS3NTwLEzK8P8icn01ZuKwaEsi15+9n6yJqOPPnp2/MayB7TSXxPThpieeCvv8uz/7jJz8vaQsNAJLx00JXXmoxuv20I65q6hsly/9JuIFMdjAZEmhjbzGLz4jKyt8vyO0MJRxdMLi3zlMOuUubbE6NkAgLiWJdvS/jDaUJ5DdbaJK3qp4c3hMtLEigeim21icLeIsokHPsVc07tr/Cj0EjzPYm/7mpS8SCLKy2omACoE+Aq+LkQo0iNUHH3wg7g5SBLVnCClhj+WH9MsgIe9UrwL93XffCWEt5A2D8MlcVXSqV4GWodAgjUYETJJfqQLdq1cvuummm4S3FcswYe1duSRTTcciHvLQH9wHntpXX31VeGezs7Pp/fffFyQSRBIkE4RTkk6UIT952bJlogcQYSl8hVxqeMX1wlfw0MLji76wxzrC8N4iRBlhzhDbghcdYdrw9sJTD5Ped0w0IHQdewiWyfxkqQKNvGMsg4TJCHjFsYQVvOjSonlGKF1Lw8QCcrdhyH/WC32Jilr6x75iLnvRqngiglWLbQf5h4lNqBV7WH3XanVTYoKTVb2d4keL1+0i974dZM1pUkuf5ugNy7mOcWRvurQGLIKFzchMJjOT4KVkq9fQqDo2yyp2Bzy3NZnzf/+1l8q2JFDVLlYlt2PdWg8lNuLPl2ZVZI7TTNTwxANVMmFOygnoI9ZPQGwdnF4S1vhzr3TpMso8/bSwzWK5cueC6IQNTSz9vmfpBkptVD+W4Qr77H9NWUMPjGlEfc8vp77/YkG7xm5yOkxUsDaOfvo0mTasyqbU/A3U8yy1TFxYILlyzpSltH9vGll5kjr90IoDxU4LL3NmpuVvTqdL7j8/UheqnhFY/8dqnrzWfJ9oUHE7XLRr9XZq1jVfU6oOawsCigAfpldCegGjUYEG+QXpRZ6p3iA+9eyzzwYsgwRhJHgtjVSgofIMk8sgYRzS26hXgZbLIMmx4jo5FiMVaAhxYT1h5LhCxAvkvCZjQf96AyGFRxwEF6HLMIRfwwML0S2pAi2vQ5gz8pClDR48WB4KcoswZRhUoLHB5HrA6AuEH/fEBgylIf8XGyYGJAGWdQi5xhrFsMsuu8yf+4s+YBdeeKF/GSSQauQRYwIC6//CavqM06ZN84dZY53humTeymrSZmVvW2qqb11lo2cAcfNWRSdiYnT9sVzmrfK9d6N5RngrvY669T6J5rn+X22c/D40BX6dmW1eFnGpFFvYvs18nUvhqcfIU8n/y5qJPX29OGfPpYcFGpWFRsBdFR0++BHtirJt6Lsd2zUVxZVkrzQz2U0Vm/5pUzI8VFURIUdYf1GMnleV+3AC4T3gqJ7ABxyVh+piFJoaPbY7VB71oV4cCssa4XkkGwf+YjiSdz7G7iW9kNGoQGu9hVoY0Ac8nSBqIJ9yGSQQw1Aq0MhPlTmq6At9gOAiBxZ5qnoVaIRDI9dVGsYCEhxKBRp9gfyCeGKLdiyRVKATEhKE1xT3hvcTXm+YJOfYQwkaqtPSQJrhAYb3Fc8RSm159erV4hL0gRxfmecLry4wgQf8oYceogEDBsiuA/aYOECOMO7/0ksvieWQgIFUgUa/8EJD6RkeW3iEMekgxy7HFekZ5U2h6C2trqlAWxvnk33pHB6+8QyofC7svS4nWeo30Bap40MIWHJbkBNL8sAbGcFMljgyZ6plUwJgSmKxEc/fDDXzMEFJVF63ADz5xJaTHVZGTLZPzMuTh2pvgEBq02wq3xs5TBwhp2lNsg16UEUSgVbdMyjhUzNVlRmnLLhcVdS8nYrkkHiF2rs5Ei83vorKDBt4KcdSJn7TGEULGl4Sw4VZzbJp15odhghUFFdQbptGhnWq8OgjEDjtc/THU+dHAIEluSHMF15XhPxCBXrr1q3+5wPxhLcYG9pDLEqSH4TmYlklaVLkSq+8DAEsqQIt28o9VKC3bdvm93BCyVh7f9kOe3zIIe8WYddQl0ZYMrygCIeG91XrdY12LHgGeKe1GwgowpMRugzlZoQOox2EqZDrC5NKyhClgocWZBRtIOCF5y0sLKRZs2aJttq+IIwVqi/RmP8AAxjIajiDBxlh2TAQ7W+++UbcG/eDIVcYnmGEhGN9ZbxWehXoaJ5RdFbH/3iadmVsgvOx9I8FyKu8KWROUiqnemxwbus2kEwJKUZVwWXsSbfmHx9cHsMlpnhWzk75e5MCprQWZIqLEvsYwtjEooSpp5xKIaL7+HOUP0vZe57UQoX3hXtbtL/0DLKlcI5IBLMm2KhBtzYRWsVutce7n3r8axUlJBv/bEVucHq2hbJafc3vTRVpFOqd4nG6aO5pA+iM9G0hmpjo3JzttPiSq8hzSPckRENVzAgMfviSkDj0vupUSs1WyxaGBOgoVxh/khzlQdXl20sFaOyVCvTfU4EGmXz77beFuBXIMbytCDM+++yzxVtDhicj1xZbOEVpXIBc4GeeeYaQiy1zi/XvMXiNMZkAw8QFQqsxSdGQ165FaDXyf5UKtB41oiVjf6XyKlvIH8nyCvw42VSQQvsXrJBFaq9BwJrflSz53SG+GdESz/83R/vGRWwXaw0sHW7iRPQaEllub+40LNagiup5y/cepG/HLiEHK+vq5wxBihE6+fv6VNo2b21U/cVqo/aX9aPKKqQmhUeg860XKeGhEBB5vW6q8nzOBNdKL/3pm3CJi+cvlUOWlGamJu3i6e3VrXlGv5LFnKbJKrXXIbDljbf4veilpgkVdEfbLSIHOMHiJmwZcU66sdV2yrLyb6vtO2j35G91V6tTPQJdz+tJN344QhTHJdoIE1nxKQl00rWn0dVj+DtJWa1FQBHgI/DSKBXoviKcG6rVMqRG7gG/zFnGHuUIiwYJ1gpFoZ0kvvmHFLJlH3Jv1BfK4G3GckkwLH1kZFgiCd5uKE/D84wQafQLJWx48Bs1aiSINK7V3g/nMvQZxzBZL/co0z8jyuq6Fa/dTGVbd9GarQ35+fC6BT8RfvS5WVly5eaGVHzASxvGTw5upEoEAjsSBpGLyYYRjmjgYpXOjdubUWX2yQoxAwTMba9kISuE2Ff/MDZoVl3EnnRKyCRzq4ury9SRH4EF700hs81G8/Y0pIKSNKp0cXoN/y873GbaXZ5MC/Y0INa1o1lPfei/Rh0EI1CwaBNtcyTz9wJH6etIsPh85LIdlTaaPXlx8MWqRCDgpi2cZOPz6mY3s9H4HW3p8oezqeegFDr54jS66dUG9Or8FmSx4n/fQy5ay99JKhdY//bxOJ20h0mt91DefjaLVD7UaRPd1mab2Ea228LLnPl0PDzsXNj61ljOLPmbqSX6mx/D5+37dyF7choV81uuuIoV3ff9H3vnARhHcf3/d3c6dcmWZFvulnEFAwZsmgHTA4SS0Ds4QAKEQGg/SCiBX35AgIQEwj8EQgktAUICoQXbMdiEYsAFsI17N+5FltWlK//3mfOc9k530skxtqTbZ+/t7szs7My7O91+5733fXUy5vLjO/GMO8fU3BjgXfA+uizQTUpOhSG5uLjYxOeit5deekn69etn3JCXLFkigGhIqJBU+gJEkyoKIq8bbrjB9NU0mqYjyLBw74Yxmv6xGgPEcbmGDRsQDOMzsrNZoAHZFRURxt/GDvRjgzU31NCoDxpemTZvoPTpvkVKiyrVck5aKxYCwlJdmyUrN5ToXlMrqGyaPtvs3ZfmGlj17icy6+sRMmjgOunbW9Oh8Cxn9CjSqOycS5b1lPVbukvulM+kaMTg5h2keYknI0f8p74tjS8OV7V59F8c2nDoJyReZYIuEP/Zn2qqpCZGdkeTtD9c9K/PJKjfbz6Iq6q6mC2RUjbOWykN1bWSmde6m2+i6zt72ZwJXypxU6PMD+Uq225ACjWnt3/738gaXfAqb/SrxdIry2csMSlT/Fn+zq6SNs8vGFqp10SAGRcXlWYoS3FL8dLKbSJrxCcD23yvznxB9fyFSmzHD0uTcNolU1eyEokuElYvVGbyEXsmqnXLtmvg3u/+WqrKq3VB0FoBPHL/934rf1j8W3G/z+33Y+IC4J303lgroMsC3cRInUi1iRiSy8rKmrFA33zzzQKJFPrEeoqQ8/eRRx4x6aA4T9RXIkZpSKks8RdgGoG8yink/8VSDxh99dVXzUY9+ZfPPjsS4/FtsUDjnj158mQzHKzGpF7qCNKwdZsE6+wqu0dXPUt0Kxa/pj/yeUPS0JihFo9YJ5PGyuqOMLXdMsa6zVv1vmrlVWs5YDcvt96kkqpv8OtiDw/FPLgEpHZtK6lpdsvo28dNPXk9xT9uhWx56kjJaPhG/4YENEdj09hI91tXlyGBrDIpPvdfGvub21TpHsVooHZraszkvswMqdm0zQXAMdprOin/ZvP2NCkeqVCwy5ZIfP4MqVI9FvUpSVSd1mVhSe2z2KQkMm2oxTgW6zVVp+lRIwvt25+nUlKBtg2oN54rLWtg64YKB/iNtK1XNnKYtl0A3LLudmet49Fgdw6j49/byQINEzQb7rQAG4AcLMJYL5FUWKBpZ1mgOXayQGORBMhlKEmJZYEmvy3iZIEmfhUWaFyHGQuETmxO19x4FujjjjvOMEgzbgieAPaWBdqyTacyFlyYly5dGrNh3QSMYk2F6RoyKQAm5fEs0IBdUjO99tprcs0115i5EQsMKda6dZGcn5aV2bIt0whGaWtFtYsSlMMEzfix6CJOkjHOAb2QhDE3p0AEdtttt5kiez/6tbmIk7FAc0Eq46Idc7KgG+t3R5HMroWaWzXeeuaRxkCG1DUQF9z8z4s/P6+jTG+XjzO7RImcouKR6ppsqdiWp4AtU0sjT3IQE+X0asnyEe0gbQ88WWqtLHxQxr85SmbP7CUb1+dJ1bZMs581o7e8+8/Rsrr4QfG4eX9b/IzkdE2NsI5cl7ndXKKXZMos6luiizCtI7GgkhPlu3pMqEaPpPZZbLrYq885rkdCkz4iR37SRuKLn6po24yuzt+lVC9Mr3b99+4rXl/s80711holbIt/PkovvbT32boW4J38DsHobAUXWpiPYWSGBXr48OHSv39/Uw3wBGAiADYA45QpU0z8KWzG5513ngwcONDUJ2NeptKyQMcDJ+556KGHGhZo+oUBecyYMaa/+BcAMSzQWE+xgpICCBZoxk9c7AknnBC9JNWxMAenNZwO/vKXvxgWaAA51lRAMACzT58+JscwbSwLNMcIQBW2aOSkk06S5557zrBA2/y8X3/9tRknbYqKigx7MymgkPi+pk2bZuZpKh0vMDZDulVaWmoswywCIP/4xz9k2LBhQm5m8vM6WaB/8pOfGHZs0kfhXs37Z+9Hu7aMq6yszNyPFxYHOop0G723xgj6HVbg1kfe7cB9Wm+Upi36nXykbFv6jQTJv5pE+JHtfdTBSWrdYquBISceLFN+1VcWz0n84Py9Ew60Td19Eg0MPvFA2bpyvQTrW44B7D68v2v9TaJDivf+zn7yn6fek5qtLXu/lI0a5FqLkujR5+0vgdBMrU3+tzH2UoIcescWuWeSN2xI80D0lvSiLjN5Q91wm5ZURN3Vf/qhfPaapspUEizWF+prGuTBaf/nfp9bU9xuro9dstjNg+kMt3dZoN8xb+Phhx9uUjuR3sluAEO2TZs2yUUXXSQff/xxNCYXoiqsql11tRGrKvG/d955p0nLBEBGcA3GCjt37lxz3lYWaFI8AXLjBQDMIgR7gCwAmg3X6/nz5xtQvX79epcFOk5xXYYNlLz+veJKk59iLR586enJG6R5zcCzvqPPdzWqheSxqwU9u7jxvyl8TvK6d5HLJkW8Yvy5kVX4zIIcyevRVa6b/aTkFCUGxil0nTZNDvzRqZJV0LqL+JF3XJw2OtmRiQ4eM0wGHjioVSvwGfdesCPdp8U1PhmgPjARHonWJ6xp4mSYAhHX+havK0jtSk8/TUM/Wrd9eXSxtfT07ylXQsdZlI+f7646zy/Kk7+s+F85/+KecuYZBfL7iRfJHgdEDFi7agzufdquARcAt11nbb7CZYFuYoHGCg3BFMRUf/7zn41VHKCJJfuggw7SHy1lGVU3ZEDzJ598IjfddJOxsKJ0rK3UWYupdeW2e9okYlsGXH/22WemLyy38TJ69Gjjao1VHvdychJj/eUcSzPnWO4ZN+K8H+dOV2tnvbNdonHRtqPL/r+42pBetTYPCLG6DSsVrMauJNbAupdele5dGrQynsAJSqewpqlolPyGjbLtKzeVVGINxpYW9i6R8yY8IEu8ubJQ49GnrqmTM1/9hRT0LIpt6J4l1ACLBZeMf8DUKcd7XJuwZPhCcvrTN0r/MSPi6tzTeA38+G83aWxvsbIU++KqwpLTJVf+5727ZMD+7gNznHKipx6PT7K8p+l5Ko+sIcn0Hh+91j2I1UDfi8/W1Ec8yyRfaDV1oaD0v/T82Ivds4QaaFw0XSrvPl4OrX1Bxob/JpnP/UCqX/9NwrZuYfvRQCp/TdrPaDvoSFpigX722WcNEITc6YILLjCuwMTD7r333sYqOXHiRDnggAPko48+MtZRQCKu0bjmvvXWW8YtmJjYTz/9NEY7p59+urkOV2hiW2E5BujFgzV7EdZUYm5xSQZ84gLNOJ5//nmZOXPmfzUWew/2uBMz9vLyctm8ebNhcsbqWltbG431xZ0bN2SssoBOACyCHsnFy9wQJws0btVYaR944AEzR+ZpAStg+5VXXpHvf//75rr4F2KpiQ3G5Zt8vz//+c/lzDPPNOcLFy40rs3EVlOHWBZoLNnoHfdrwLKVVMdl23fkfW5ok4zYU4k1jCT+QfV5g9Ilr0aG9N/Ukaf6rY993ZvviCfQKD3zKqUwq06yFPD6vQHJyWiUouxaKcqpk6B6R6zXdq60roHa6jo5d9TPZcbyCvm6IiQr6z1ywUG3S01Vqm6Urd+js7fIDZfLmQfNlkGlmyVbc4Rm6Hc5U1mM+xRVyPGjVkqf7G86uwp2yvwguPrpazdJoQKPTE9I2Yl1AUH3eRkhGTV2oAw6eOhOuU9n7sTn6Ss53h9sn2L8QgLF6n4qJZLrvd61/rbwQWico553Q5S1XZmfPfoZjAXCYX2WCSl/iRoa9qyW+lkftNCTW4UGwnXVUvF/pypRj5KuhZrYtGvfeUwaF89wldSONdD01N6OB9mRhvbPf/5T7EYKn/vuu88AXEAYMaxWsAg+/fTTZnvmmWeMy+2qVasMQHz00UcNmRWgd9u2bXLkkUcaC+l3vvMdQ3xFH+SzhfDJWkO5p1OwPkKuBHCDZRhwd+utt5pzZzuOGQvgl7hVZMaMGSYW9a677jJ7APH48eN3eCymU8fL/fffbyzAV199tSGmAuji7ty9e/doq1tuucXE2BInfd1115lyXKQTsUCjW/o666yzjE5OOeUU0x6AjeA2HZ9T2FQ4XrAuI6Ra4np0h3zxxRcmfprjeBZogDi6wd0b120AsW2H1b+1cZnG+oKVm4UNNsB1R5LA+lWS66uUfQetkqKCasP+DODN8MEEHZQsfWjuX7pFhvXXWMKNkQWEjjS/XTXWcCAggYpt5nb61ZVcTZdSrIC3W26tdM2ulyxl1jai39XqxUt31bA67H1CwZC89ft/SaY/9kE53BiUCU9MNH/zOuzkduHA6xd8Jdm5Xjl0yEo5++BZcv6YL+XcQ76SY0YskW6Zm6Xmq6m7cDQd91ahTdNl2dPXSZecRumeFZCemn+1VPdd9Xu95qNpElz8gjJFb/+Od9xpfusj93pKFQT/ROoqR8iqxXVSvS0g28oDMm9mjfg9R2vdZQrqWnfb/9YH2o5vEFi3Qq0HVdKn72Yp7ancJQW1+nzUYDaOe5RulV69y8VTXyWmbTueS3sYWuCb+eLJS+BVpN/nxvnu38f28B4lG0PrgQDJrnTLYzSAVRbA9dBDD0XLAZ+AuhNPPFGuuOKKKAs0sazEm9r0OlxgrY+TJk0y5FBYIS0L9Ouvv25cfwHAxKRi3aQOoGxZoO1NAcRYKunPskAzJligIdYCrE2fPt02N3G19AkAhMAL11/iX7EWs1EGmIYg69RTT5VUx4JV1zIu25vBXA3JE3HAEGCRmgiLLZZbxuUUywKNyzJWaQDxsccea0i6bDuAbUCBA32xUMA9yacL+zSSyNptWZ732Wcf243ZY2WHzOqQQw4x1unKykrj+vzTn/5UXn75ZWOdtyzQkIsByrH+Ys1mPljY7f2c42KOvB/0jys1YtuZE3055phjooRnuIV3JPHoZ1xCjZKj4VZD+23Qz53+tmranqDmtczUh7tMBXIAOiNh9wEv6XurHgThUGILevw1MEG7klwD9WrhffLsB2TuzKUSVMDrlLqGgLz34D9k2dufyfXv/V+M54aznXssUrtlm/zngb/JXnnK3p/gI8fHdfarn8j+318tRYMiPA2u3pprILj63xL85Mfib8xWpthB2iB2UYbwkOD0n0vwq1+J//tfal7qzOaduCVRDXg9hZLjP06+P/xPpsyrDNtZSj70RdWoaBv3ILkGvNm6QOD1iUcBWm5ug9kSttbfdtM2YaVbaDXgySlQK1Ls74yp82VopgGXHd/qqT3uE/ystcdhtv8xxafVYcQAHdyTsawCMHHfRQC/ED3Fg528vDwZOXKkJGOBBkTHSzwLNFZh3IsBseecc04zFujFixcbRmPbD2MB4DpZoGfNmiX33nuviXuFcArr8F577WUusSzQrY0lGQs0sbRYOUnbBBBHR9z//fffN0DQjsvuAcI2dy/6uuSSS6JWcIAnrs8IusNtG7B58sknmzLLyswJ+gDIAraRF198UQ477DBzHee9e/c2wPnSSy+NulxTDsBljIBqLLoIixS/+c1von2xUAAAt/djXDBpk8vYzpF5WBZu2850pi92vJyTDqmjSLhO8wC//VN1OytQh76IMwme4Llq3WguYfF5tkjjzL+L/4Czmlenecmm2UvUw0AfSPwtKwLQsfSzpTJk/RbJLe04KbNantXOqyWVzG39LyNQX7LVClyov3BqIDLCOkxXPc+sb5A1c1bIrw+9RW6Ycp9h7tx5I+gcPTVU18oTIy7RBayg7LV/4oUZMvusKe8ic47+qZz71v1SOnJw55j8TpxFaP1HEvzgQtPjngdsk4/fHSCN9T5dKIz8vfSpC/R3L1ygi4ga+9+wVQKTz5GMY/6hhFmxIHknDqlTdPXVxNly0qH7ybtTv5SuWTly6omHypIvlsug/cs6xfy+zUn4B+0rnuw8CddEPI6S3Ys2tHWlZQ1k9BkqFXmDJbfqSw0RwaVcv876J9NbXyNZY85s+WK3drdqIPJXeLcOoXPdnNQ/dsNdF7djLJO4+BKLa4WYUdsOKyuutAcffLABTrQBQCEA1gULFpi4V+JicbUFYAG6yPGLBdSCUnPB9hdif3GpdrpCA2pxd44XxoKFF8HNGGsrFlCAO5ZoLL9IW8aSjAWafgCPxPUSX3zUUUcZy/jdd98dBbO0cQpx0IkEPWBlBnziav7555+bZk5GaQqYB9ZyFgTsogNtcPG2giWd9wiwzfuBSzn5h9E9ggu1XcBgPPTFHhdt3KZ5HyxQZ1yAfDbmyGIAIPe9994zc+U97AzS8N7vxJeXoamQWP1s/rmKnaMCki410vDPWyVca2OGY1uk61ldeaWMv/R/ZXNdrvnhbEkPgI7yKp9MuemRhN/llq5Nh7q//fRJQzQUVvCL7JPnkyE5HumjRjX2nCMhtQxvXrFRPnn63+bcfYnVwMSfquePWoAaGn3y4VyslpGHOtvKWH9X9pJN2/JVl/ob9MMHJVAHgZsrVgPhYJ0EPrnGnkqGPyyX3zZdinrU6rEudhU0yHcvmC9D9tkcaRMOSHjLbAkt/Wv0GveguQaev+0V+d3Fj8vSqUuV67lQutdmyGevT5cbD7hD5n2ysPkFbkmMBny995BQ1Tb9/YgpjjmhLlSjIU09y2LK3ZPmGnjurr/LtY8Vy/rKbGkIeKWmwSffbM2TH792mPxg5C8kGEhgHW7ejVuyGzTgAuCdrHTIq+yG2yvxpLfddpshbCKm1wrA1LYjdRIuzVgwsWQiuFQjgFvakjYIIDd27FgDpmbPnm32WCXffPNN4/JsLtAX2gNqbexvz549DZjDmmstoLZt/B4CJ/L10gdiiZ84bstYsPQyJ+eWm5trUh9NnTrVxOzSBpdoACHzwEobLytWrDDjiS/nHIsq5Fnnn39+NKUSAN/JKE07rNtYcgGt2dmRVAqMC2Zo+kcAxoBYiLJ4PwC7uIwzZoSYXkAw5+zpC6supF7E7iI2XROLCLig49rMHDnHlZv3hGOrW3NRB30JN9ZKYOoz4gnWS36vap0Fn5fkv6g5JRqjlaOmOL2ucfpLHXTW386wF/5tkjQqYVN1Y5Zsqol83uLvZB9Wlm4t0s+pRzbMXCDlC5oW1OLbp+P51jVbZPbb0yWgbs5O6ZXplUE5Pumpe6cA2CaqO3Tlhq3O4rQ/3rp8raz6cJZ69UUe3DZX5cv4L4fLqk1FuviSI6u3FMrUBQNl3jc9o7qqr6iWRW9/Ej13DxRArHxTJFAVowp/ZkiuUBB800MfybX3TpW9Rm+MqZdApQS/ZnErsoATW+mejX/iPfnHr96S+ur6GGXY8JH/PfFBWfn1NzF17kmsBra98kcNUcrS5xD4X2LrOLNlwYBftv39ieYN3JKoBj587XN54ZevS31dSG568xC5+h+Hyc1vHSy3vn2QbKnOkvXLN8rrj06ItncP2pcGYp8I2tfYOs1oEqVBAiDBTGw3mIRvv/12Q7RUWFjYjAUaa+7atWuN2y6xsDAcT5gwISELNLHHV111lQCSrUttayzQKNsyQeNWjEU6GQt0W8YS/ybiEgxwPPDAA2OqsBjHM1kDSBkHrsqJBPDZpUsXowss1WVlZcba7mSUxjqMFRciMacAiBFAMALYBaASK80YIbSCpKympsaUE9dLX5yjpyeeeMK0Ycyka0LsYgHXAnJZMLDs1Lh80z99dAYJrZimmDdiTfP5QxoDuEV8WerOjguQxrQBhjlmy+leLTkl25l3G2o01u2NzqCCnTaHpe98LIHayAPdlvo8WV1VIA1Brz6keCSgG/safRhZWlEkDaEMc1/A26rJTbH8O20wHbijhZNnaxx1W4GDRxZ+ECH/68BT36lDXzH5C42djg1j2FaTI58tKpN/zxouH88fpCA41oulflu1LP5XbCaCnTqoDthZaNU7uuBX2faRN6iHTOXytl/Xya/gmekf97/d4izra+rlzYfHt9gmnSsxENT85y1dmFHSxTp4JyKAF9Ab3bQsWK/QQP8G1EzRtq4k1cArD8R+HquV/2RLTTZPP+aaQENQXvjf16XWzTyQVIe7syLyNLU7R5AG906UBok/RJA3WQEUArIASbACA2KxZAKYTjjhBEOghKUUayNpewBwWH4hkXruueeMy66NMaVPGIo/+OADwRUa0iYskbhjA/T4IYkXC34hnMJyDCAFuBPrCwi9/PLLd3gsznsRc1xUVNSMjRq3YViWAfaWsfmpp54ysdIXX3yxTJ482dlN9Bgd4lJ9xx13mDIsyozXMkpbUEr/TkHftLVEXRCMYZ397W9/KzfccINpSqwuuoYwi73tCzZvyMKII+aaI444wrhD276YIxbt0tJSY+mmM2KoWbhg0cI5R+pgAce6j0DixXXtXcIVa/VHtGkVHixc2F9jgjW+LVCnf1b0B9WrwNivrKfbcXJ0SuEKlw06qgw9qFodawWqbMiWyoYskybFq4sJAY0XDMXlvww1NMrWJa4enXrcvGy9QIDVFqmrrJEtKza05ZJO33brsrW6INN2d2auc8Whgaod9NDQxdNw7RrxFO7h6Mw9rKmolcrNLS8ohIJhmfex6wad7NMSKt+knh3WQ0azezdGbGCeyM4A4si1EQAXDjZKsHyj+Iq6J+syrctXzG39N9jn98rK+Wtk2Gj3+9zePiwuAN5J78hAdVkGrBLXawULJrGmxJcCiGwaJFigYV52ClZLNkiYcJ1FcNPlWuJxAXm450K2BSkTaZBw7cWlmTZWsIIC1ADYpPKhPwAtsaq4QgNssXBaYSwQdcWnQaIdxFDxLNBtGYu9h3MPyMVqi9AXuY/RnY1jhkmb+wLEIa369a9/bVyLmQdAHx3hJs5CAcJ8LQs0Mc6QjTF2KzZ1EffEMgvAJKUTfTz22GPR1EW0xyrNPXjPYIvGyktKKFIsIbYv3h8WE5ws0FiSnWmQyGXMuJws0MwRAGznaDrVF64DNCO8tx1BIi56WHqbRJ/bJCNbUyDp1qIkWIBpsX0nr+S72lzU+hvWVYVEVdsb2zjX5temZwmpj9osqt8duq7NN+o4F4R28G9Q263vHUcnOzLScCJm2FQ7cl2gm2kqrDHSHs2L3pqEwzWtNUnb+jB5amMI1rYD3WR/OhUZu9/r5B+XxL/dce31NyaldnGXuaffvgZcALyTdAwwJZ/tX//anMACS+KVV14ZjSe1YCf+1lh9LQDDpdcKrMMAMoivLrvsMlOMpRKrJGRNxKxaWb48lgUaoixnGqRELNAwUsenQcK9mJRHAFEnC3SqY0mWBgkrNyA2niEZ6ygCAGQ899xzj3EjvvHGG+3UzB7gD5hnfE4B0OLqzH2dwv0Q4pqZC4J7MiAZS7gTcBKDTN/xghWdxQbbF7Hc8SzQzr5SmaPzHpCOWYl31bbl7W3vyVdG7AyNp1aX5raKp7D9W7jbOqf/pn1Ot65Su6G8TV14M3xSOKBnm67p7I2L+pWIPzdLGtUNMlXJys+Wrn1KUm2eFu269O8pvkxNZaZeBm2Rgt6xXjZtubYztvXk9pFwRexCd2rzVFCSrX9fXYlqwCy4FvxJspQioUY9xFuSAcpaXh/8t2T5jm+pWVrW+Yr0c9WmBWjN3tDV/V4n+7D0GtRDls1alazalAeUcLHvEPe3ukUl7aZKFwDvZMVjMbUCKCJuFwCFJXP48OHGFZl6LJgWbAGcAW5Tpkwx6YCSpUFyAiULDOPTINl7W9dnYn/p98knn4ym4bFt7B5A6EyDBAM1hFyMn7hkXLCtWEtta2NJlgYJV2TcfekbhmTcoXEBtgRYLBZgOQUEY0HHrRuL8Lx588x1Z5xxhnEhtuNhj2WWBQEsqyytIhNpAABAAElEQVQiOMW6PjMnLOKQiH3xxRcmzRNA3JmSiMUBLPX0gRX4tNNOk6FDhxrWbfq0feF6DqkV7NgsHJA/mPfP9pXKHJ1j7IjHvoGH6A9p66vxzeaWkSW+vU9pVpzOBQM1hcfWJd9IqD51wOHPz5G+Y5sWvtJZf3buw47ZVxmgX5LUtaiLYUqrPWTsCNuFu1cN9B+7r3z2u8w2AWA+j0NOOdTVn0MD3n7fleBGjYsOVDtKUzjUPMCeLkNTaJg+TRpC/9ZnpqAcd3VXef3ezRp9k9w15jtXd5GAfCm+cJmGkQxJHyWlMFOY3bNHjZWaD/+lUarJdUhXxLHmjj7asMGn0HVaNjn92hPksetfkLo4UjarDH5fDjpppOR3zbNF7r4daSDiR9qOBtTRh4I11m6jRo1qMws0gBCJT4OE+zN1xADjgov1l7hcQKIFpVZ3AFoANq7P1hUaUJdKGiTiYnEXxjJK3DCpl6zYNEipjAULK7G4zg1gSKwx249+9KMoQ/J1111niLGYE3HOWNOtuzEEU4BjGyP72muvyVtvNREz4M5NjPDHH38ctbDb8bK37ta4WZOKiJhdQPBRRx1ldNOrV69oc0A29wUYA8KJBeYa0lMhNg0S83CyQF944YWmL8vgncocozftoAeerDzJ2O9MUVNR22bgzxb/QRe27ZpO3nrPi06UDLW4tUW67NFHuu/vPiQ7dVZSViqlw/s6i1o97rvfHlLc341vcyqqZPgA6bHPIFxlnMUtHmN5H/q9I1psk26V3gGn60p3G/8++nLEO+wqVb37aGY/L+FwtQLa2XqqWRp+XiJDD82xVbF7/bj+6MmeMnQMTPr1Shg4ObbePTMa8O4xSt2aWwa/NAQgewYe4GqtBQ1894qjZdR39knYwutTDaqef/7ijxPWu4W7XwPuX9ld8B4kYoFOdlvrkgtgQwC3ANr99ttPysvL5cwzz5Rx48YZ4EtKHiyW8WmQALtOFuhU0yBZIixSA0HsxN7mzW3rWADPzhRIHDNe5oLYPceAdLtRjjX33HPPNRbVt99+W9544w0TK0xbjrHkWnn33XcNIRjjhD07XgClLEgArp0COzNiwS3x2+RpZuGA2F8YsOMF8irEOXbO7XvGMWLr7Z4yOz/2znLqOqpknnibSG5kwSbVOfiPvUk8Ba57n1NfuT2KpPfB5OFu/aHEXrfvFad1ms+RndPO2GcX5BgttqZJW59T6K7Mx+udv0/H/fYayVSrbqpyytM/a1P7VPvtyO08/nzxjbpXp5DqQoI+joUaxTf0so487Z0+9mB4mfYZCVLls3nHpP5ywYPdpXSQXzJzPeoW7ZE9RmfLza/3laN+0MROHpYKzaveObIu7Eylbn53vPKvsEjAc0nzniljq6jIlc3j323ewC2J0cA1vzpb+uYo94kSVtrNp8fFvqD88qlLTF76mAvck3ajgVhU0G6G1bkGkogFGhfgZ5991kwUUAQAg0iJPeCLHMG4Rk+cOFHIJ0zcaTwLNJZQiLWIG4aQqSUWaOsKjcU0EQCz4NeyQAMMf/jDHxo3Ze4PE/KOjsX5bnIfrMwPP/ywAaywNeMCDYhkA2QCWkm1BKDFlRs3b8irEJiWBw8eHO3y+uuvN1bdaEGCA4i2iGN+/PHHzUICoBmwO3LkyCjQxeKLYDGmT1zXyRlsrfmwalsW6A0bNhiQzGIEVnFSWFlSLvpIZY6W6Zr2vO/cD8H63FHEU9Bdgmf9VXx/PtaEFenaQVJp1ATxG/1jZcjR1yVtk84VjVsrxOfRmHSIr5JK5GlFvz5Su35T0lbpXLFmzsro9NFWIugR0WJkuWHlF0ui7d2DJg106VcqJ/zuJ/LWFQ9qYUua9MiIs4+U3qOHN13sHkU14Bt4ljI6r5fQl/8bLUt4gKU4p1T8J38knozUFx4S9tXJCoPhdTqjWFbyU24sEbZggAVleJ0SfdOxYfJ3svnCeCdTUcrTIbd33Yplapn0GoCbk1Ovz2PBGCAcCPj0OSRLf9O9Ur9sqckHjuu0K4k18OXfPpR++R7po+Sfmg7YSJY+C6n3s3zxwvsy5vKmEMLEPbilu0sDLTyy7q4hdcz7WisgLNB2e/TRR018KNZJJws04BfQS55ZNhsDC+iD3InrsOICegHEECMBAknVg3swDNKQQUF4VVZWZhQGGRbCOKy1EUsp/eAKzT1hLuaceit2LPEs0IBUiLhoT4wrzMltHYu9h3OPizEWcfQBu/JZZ51l5nTKKZG4UOJ4EWJrYaeGUAoXY+KnkWuvvdbE+poTfcGlGTdwAC0LBvHC/ADvXEdOZOb5/vvvG6CLPgCrCPpFICizYJS6qVOnGrZo8v9at2zikEmDBCgmVhp3bxYnnCzQqczR3FBf+HwwFjZ03JGkwVssb/znONmwiXzMGfr5i30QaWjwqY79MuOrPWTJtogreUea364aa0Nltfi8uoKsLKeanEJvy3c0dlM+TvFrHJwnpAtF5R3rc7Kr9BifBilWgxGNOsdSXx35/jvL3OOIBgAVBV2zRX8xtKD5xucxyxOU/G4uwGjpM5Ox1zWSccQzInn9pL4x2ywW2vZ15GLNKBBP/1PFf9L7Lvi1inHsw5I8htqXoeFeCcEvHeBZ1nEWlB1T/tYOg/r8ZcEsALe6Okf5S/KMRRircEVFnimjzohPU/DVJNf/tzbQDtRx5fpyXSQImYWYHF0nYAP8IjVbKiMH7mu71IBrAd5JbwtAEUmFBdppLXTenj5mzZpl4k8BnzYNEmzMyVigSYHkTINEH4Bg4maJU41ngcYdmtQ8VhgLIDEZCzR9wWAN4RNbqmNpjQUa66q9N0DeuhzbhYSf/vSnxv3bkmPRHiA7e/ZseemllwwJFXNgkQCXaUirrNx3330mfpdcv1hoiZWOFyzKWIZxLWfBwebiPeyww4yrNveB6AqLMKAUF2rLAs0YAd5YayHrwiLMooIdu2WBbm2OdkxYwwHxiE2VZeva+z6/l+Zv3uaVCZP3lW7FldKn9xYp6lIt/oygVOsq8uq1xWYLBn2y/wm92/t0dtv4cnsUS8XiVeaH0+tjgYzHN7uYEDnC0oFk5GRJrurdleYayC3Kl7aA2rzi/OaduCVGA4X9lA1an+QydWHGLKpqKTCYj6HZ9CVDY3+7DHS/10ZhLbx4+50i/j4nyqqJr8j0p3+veerrpLomU9Zt7CpX/O0Z8Sg4diWxBrwe5SThg9dm8SoocUMcnGrzKcloc9El19D2H5f4So1F9+Yluia+Yfqe9x45UOb881NprG2efaDniAHpq5gOMHMXAO/kN8llgd4i5MBNhQUa1+FELNC8JdOmTTNu3RBRnXjiifL5558bt2OA6MKFC827BnC+6667jLv4nXfeaUA/5GG4OGPBxtoL4HfGDNu3+7333jP3sKRjME2zIVh779E0TCwMDBw4UGbOnClLly41Vmvq28IC3dIc6QsBKFtJtjhi69vb3heslZxAuTLvZsumLYVmSzRGr7r3dq1ZlqjKLVMN9D9xjGyYNkuCjZEnPcBuMpZOj8YX9TpsP1dvCTQw/PiR8ulz76dE8oLlaM/jXD0mUKMpKhreX7KLC6V+a5WxbiR6RA5oyqkBxx2UrAu33KEBjzdDBp14oTTkj5aX7nhVykb2l3FPnC+erLYR4Dm6TItDn6e/NIazdK7NAUbLCgiKV9zFGaeOPGrwyBt5gFR+8oEWJ/pGO1uL5I/cT5nyXUfRWK3Enu1/7pEy4a6/RNQZt1Bz8q/GxTZ2z9qVBtxP9k5+O2zMKHuXBXrHWKDXr19vyK722msvwd0Y8qyjjjpKLrnkEmMpBaAiNicx7s3om7haUhERv0zcNVYLACXXUw+jMxss0EuWLDF1Nh6a8xkzZph+schi+XWCUfp2WaCNemJeVt/zMxnSHbf15D+muPR2y1U3qunvSt2ieTHXuycRDfTpqyQacXFuiXUTlqLcCsnvEvE4SdwmfUsPP324WiwbUlJASN3WDjtzr5TapmMjr3oTHXL3D1uc+qBzvyN5vUpabONWxmpgz8OHyS+n3CGXPXKJ+F3wG6ucBGdeGaC/Lm1k09YrfDJIF27ael2CAXSyou4HBMTrj0NqSebY/YjSJDVusdVAdmGu3L70Genar7v4czKVDDBbSgb1kqsm3ivFmpnAlfarARcA74L3xmWBPtq4c6fKAm0tvPExvdYFGesuAlM18clOUizKseriUoxLMq7jtIHZ2cojjzxiyrHw2hhqYnohv3r55ZdNjPAdd9yhP54ewcoMKRftXBZoq8HIvn7VCqlbOFcKMutldN9VpjASv2rbEdMalJK8GtmzxwYTS7T5b8/bSnfv0EDDxCdl1LAV20sSP5x41IrOQsN+Q76RmsmvOK52D60GCha8LkfvuUJdybezkdiKuD2EY2cdOE/y5r8RV+OeWg2E9e/jhFufksqAxvbrR5LMKc6tJuiVuW9/LpVrNtlL3H0KGvj0zRly23G/kluPvlcCjTuQSz2Fe3SmJh6PT/yeY3VKWIFTlbAuhB2dauO0aRduqJKsynek10Gtf2f7HL5eMtf+SbjGlZY14M/OlBtn/F6+99wtctgvfyA3fP6w9B3VRNba8tVu7e7SgOsCvQs077JANyk5FYZk3JYRgDAx1ccee6xgFbYg1rotDxs2zKRAaupdH9D0oW3y5MnGLRp3adyxIQ/jPcCiTOzuhAkTDGkVFl2IrBBYsnGdhsDMWp3pC9ZpLMzEEwOGkZ3NAk0cMUAd4Z4dRWq+nCbh7bHL+ZkNcmj/5bKuskDKa3P1gdkjuf4G6VlQKUU524mG1CJfPePTjjK9XTbOcH2tBNcslcLcOjly5AKZubC/1DX4JaAAg+hfnzdCsFFcUC37DvpGvGq5bJjxnshZ1++yMXaUGzXO+UBGDVgj+Vm18vZXw40OA6EmazkLMhkaY33CiEUyrHSjNHw1uaNMbZePc8PcFdJQVWM+g9XBDHUn1eUXdb8nPl0/kZwpOW9AlkycIfuNO2GXj68j3nDuJwvl3jN/r+zFSman8dU3HHyXPDrz3o44lV06Zr93bw1r0FCb8Acp3DdDsr3jdBGsOIW26dUkvG4alNlS2L9CrcDrZN00ZdJu1G+2fezQQ58/JD1Hb5H83kog5tW0cus1c0i/o9JLUTsw27cemyR/vDaywD/h2Y/lwcm370Av7iW7UgMuAN7J2rZszHQLSRKszYAvJws0dbjnwgAdL7BA33///YZ9GWZiy7xMmp7vf//70Xy2sED/7ne/k0svvdR0wX2daZCwXhL7Sj0s0H/4wx8My/B5553XDGQxlngWaJiNia+94oor5Je//KUhktrRsTjnaFmgsarCAo0ATGGBhmALFmhyCOPKzLhIXQQoxR2ZlEmIZW42J44X2pMrGcsvJFpWbr75ZhNniw5og/Tr18/oA/ZmZOjQocbKC5s0xFuWfOuQQw4xZGK0iWeBfvHFFw2gxhJNGqp4FuiW5mjjjemX8QHaEd63Xr16meP2/hIo3yzhOv2R3C5ZSnw1oGir2WxZ/D5Y7a4mN9PJViVwy4h4NeRkNcqYvZdIBeycVblC+qjszIAUF1ZLXnaTa2+wovUV/Pj7pMN5qDqSLm1Yzy3Su+t0mbO6hyzZUCw1uqCQl9kog0o3y969N0h+doQxPlxVng5q2aE5blup5H72wVjBrjnUhS2nBJT4pXzZWmeRe9yCBiY+PcWAX5qE1Zy+etE6qdi4Tbp0d5m0W1Cbqcr0HqEM+LnSEJ4sjfWaTjHOIBxoyBB/Zr5keU/XlHKRRfTW+ky3+nDtBk0VEvnbl9+rTgadslrqyjOloTLy+5NZoGwexQ36HLJdM6GAhGuaCEbTTV9tma8Fv1wza8o8KV9fIUWlXdrShdt2F2vABcA7SeEDBw404O2hhx6K9ggjM6ANEieAJC7ASN++fU26nbPPPjvaFssfeWaJawXsJmKBxpJJCiQAHgzRsBfHs0CXlZWZfugPIBXPAg2wJRewFcZCn/Es0KNHjxY2SL0AZXvuuWcMC3RrY2mJBRr3ZOYIoKYdjMyQTCHMjXhbGJhZCAAIA5CxAHMd43Xm0LXzABRXVlYaN2UIsSyrNPVYbwH4pFwijpiFBN4T65pOG1IkYdnF4gxLNmPDdRpgbMW6YB966KFy3XXXGdCLFbpbt27GgszYEdrZOdIX7yu5lN9++21Tb9uZE33hs3HaaaeZU9IqdRTxFeof90x9CmlInZzEm+3muIx/f735qsdgkyskDx9d82vNFt/Wnntz3Qdmqwvn3pOdp4sykbQdBbpgcKhazNmSiSfHZYlNppusLnktpJiJXOX1Z0hud/chL5kO48sHjxooE//8nwidtlbWVdVLTkETCWJ8e/c8VgN+7yjJCO8lX059VQKhedK1V4aC4bCsX9wgw0afJL32UJDscR9rY7XWdObJ1O+qkrFZUZJnySlpMJsti9mrtdiT5f7WxOgkyclxlx4uk577SP9mamYV9dIq7FaQpKVb3F400PRNaC8j6qDjwNIbLwAdctl+/fXXBmBaEiVyzeL2Sv5Xp2AxHDlypGFQxlILqEbeeecdswcoxQtWYtx0cfVFcNPFCmrTIOHaO2XKFHnyySeNhZi0QJZEivaMBYCLNRmQCygkFdO9995rLLFz58414BEQiqQ6lpZYoOfMmWP6Yr4APgAi5FQIgBfhfN68ecYtGQZoBN0AhHFjdgoAGivqggULTPzv8ccf76yOHrO4wFxXrFghV111lbG64iaNDrA0kxLpsssuM+DXXjRx4kQTYwyxFkAXoZ/f/OY3Ziycs1AAYLZjpx1s1IyJxQY+B4Bwa6G37bgW4XorDzzwgD1s9/ucYSPEq2A/1AYAnDM08jlq95PbhQP05inhWpduEqytSu2u+tSSufeY1NqmWSv/4FHSoGRrqUrGkANTbZp27XodMCSlOZeN3Teldm4jTXM3tlxmH1QrH36WI7171MkvH+wl/iBeC91d9aSoAY8nR/Y75mL55bH3yddqacspzJHLHr1Eeg86IsUe0reZp6f+vVOrbsqi1mJPadPzScrXpWHDm/58pWRpWriAhoX88DcXqBenri640q414ALgnfz24K5rhbhOLIB/+tOfBKskLs+49yK49OKSi0D2hCUUoPr++++bMgAUABjACrAjVhULJ4AK0Famlt6DDjpIHnvsMQNKL774YnOdfeGeWCpx9cUVGssnoNa6ANt27BkL4BcBXN5yyy0GvAHcb7vttmhu2raMBUtzvCsvwJANwA5ghIwKcNunTx+Tx5d7W5dk7v3WW2/JNddcYyzPWKghpgLcO0mvcDvG3ZmFBuYG0Ewm48ePNwD/iy++iGkCAOY9IPVSvNAvluDnn38+miYJUIyFmrzIdlxYfNE1whyxJjMWrnOmesILwM4x/l4d7Tx7+N7i61osoarUkr17c3Kl6IwLOto0d8l4c0/6gVS99GDUetniTdX3L/c4V4+JdJT9ncukce7H6ra3LVF1bJnqMUfbu5JYA37NN33EbRfKxJsfT9xAS/c843Ap3XdQ0nq3okkDgffukOCMJ+XGU2t0216+RrnfH35WMn/ytXi6RJ4Nmq5wj5JpgIXsu96/Xaq2VBmLW26XiHddsvZueUQDnpwS8fQ9XMLLJ+CD34paNI9yv6OEa1xJQQN1W+XqH2rMdN028TWu0QtSW0BMoWe3ybekAXeJYicrdu+99zYETOxxeyW2FRCJi+5HH30UvRt/wG1bUvTg0ow1FKso4rT+0vaiiy4ylmRS+GAdnT17ttkTW0xeWid5Eu0BtcT+Ug6pFEASay4W1JaEcf7lL38xllLaYZ21gvU31bEA9JmTcwP8YQUlNvr888+Xjz/+2IBGgCuWbAA9/XNO/l9SEZ1zzjkGSBIbbS2l1nWZsf7whz80FlzczS0AteN17rESA0pXrVolI0aMcFaZfs8991xThls5rsps6BDdkaeXvnG9Zg7sneNi3AhAHgHIo/djjjnGLHhwDmCmP46ZY2cQ5tHrhjuUJKd1gT3WU9BF8g9xV+kTaSv32PPFVzqA1ahE1U1lgN/jL5CMvu6Pa5NSmo4y9zpMGgccJI3Blr9jjUowFtzzOPEPVYuIK0k10G9IkQwo2KoEWNBeRR6YyU8Ni3ZxVo0MH+a67yZVnqMi8PljEvz0EV3tjqTwi1Ztt8Y1PH+Cy7YbVUrqB/nF+eKC39T1RcuMI38j4o/1okvcQ0gyjtbPrCutaiC0ZbnU/uYAaXjjZml89xdS99tREvjy761e5zbYvRpo5Wlr9w6us9zdAjaswVYASK+88kp0e+GFF+T22283REuFhYUGHGOVxNoIkAa4rV27Vg477DDDikxKHtiMTzrpJGP9hITJCmAQF19AMrGtCK7QAMj4+FN7DXsAJbGyuChDfEUcLRbMmTNnGgvpjozF2T/HAFmALeMnzrlMLdlYvwHFgHQESzfngNb33nvPkEthvf3ggw9MPS7MyLPPPmvqsLgST43FFddyq1fmY4U8vwiLDVjGnYJFHbdpLL24QTM+NgAegNfqDKs17uP0CyM11mf0/sknn5ju7GIB5VzLggG6ZNHhwQcfNACYPjqTeHrtIWurWyYcCSoYqW/MkKoh3+004H9nv4eerBwpufefhhgnFEoM3oIhr/j2GiuF4+7e2bfvVP19UHOirNjSVRmgE+uxVslyvl7XXT6uP7FTzfvbmMyKx5+R7jm1MqJko/TLr5QeOVXSO69ShnbdInt0qZDVL/1DQvo75UpyDbCgG/zs/yVvQE19pYQWT2y5jVsbowF+l8eddYs88fuXYsrdk5Y14CnoJ/6LZ5hGjQ3NIUBjo0/CueqaP069EnK7t9yZW6uZMOql7tcaBlKzWb/H+ny3fZGr4e9XSWjDAldD7VgDrgv0LnhzEqVB4kcRIigrgDfIk7AS3nTTTTEs0CeccIKxSOI6i7URwAb5EpZfYkafe+45iWeBxmUawGhdobHI3nrrrZKIBZoxWPD75ZdfGssxLswA93gW6B0Zi52j3TNvYmhxaUbI6wvgtizPFkiOGjXKkHDhMo11FasxJFWcI4BiBMsuQN2K1SugFustglUbgRTMAlZTsP0lUUol3iPANqAdndtxsehA3mAnCzSLA3ZcuFRjmS8tLY1huiadE4sWgH8nkdd9990Xdb+GBZvrOopULl0l1aEusnxDo5R23SaZGcoaibVXJ2Atw+XVebJpm7q3z4ssQnSUue3qcQKC1/mPFe8306Soa5USQ4ciulRl1tdnSHlFkfTof9yuHlaHu9/SGcvknf/sL/v22SBHDlkpvbtUqUeGRxdfRNZU5MuURf1lzprusm+h+3ls7c2tXrLMNMnS1FE9cuOsl9svrluzTnIH9Gutq/StDypJYF15y/Ovr9CH5Tm6wHVGy+3c2qgGvn/sVTJt6mwZ/9Z/pGfv7vK9s9y/jVHltHLgye0ha4b8U9a9eKsMHLRK8gr1M6pSVZEly5f2k14XP6CeH+53uhU1murw+nnKJFakaV/ivuPK1RFc+G/x9hiWSjdum92gARcA7ySl47KMdc8ZA0xc6MaNGw1TszMNkmVedt4aiycbaYtwnUVwH4blGYsogBH2Ysi2WPm0aZCwLtLGChZVgBrgLT4NEu68iVigiXNNlAaJVD3xLNBtGYsdU/yeMQJSsYgD4JkD47Ji0w3hZjx8+HBDyoU1mvHgmmzTDdmUU+iZvogpxlL+t7/9zXZl9oD6GTNmmJjrIUOGmFjrmAZxJ8wRPWMtJhbbplSy4+L9YTEB669lgf7ss8+i46IdpGR2jrwfWPGxCAOAAbnONEh8HrgnkohMLW547erUo94GSE1Dlizb0F1zrAYl29+oORjDJgdrbUOmySNKG9uWY1cSawCW7Iqt+WbzqJupT4FHMKgr8pp+xqOu9x5dLHKlZQ34dLEKmaUpkNhw2c3VFEikQiKvshWfvyk/sC1z93EaaM0lX39nyGfrSnINhMMbdDWwIXmD7TVh7yL9fPIJdfXZqrK0wehD9jEAmLZD9xyYyiVuG4cGfLnFMufLYTJ9cm9HqSZ2UPb3vld1iylzT1rQQIYaWVhdjRePMmhnRkIa46vc8/ahARcA76T3AeCCyzIWyniB9ffKK6+MpkHCQphIcF3Gmgtoxj3YCqzDEGrhwgxLMfLb3/7WWCUBgQA1K8vjWKDj0yAlYoHGbTg+DRJWT/LyAh4Bx5YFOtWxJEuDhLUXiy0uwZYhGXdhyL8s8Lfphpgb1lrAPALYBOxal2TKiKtGF+jMChZVYoitS/Q999xjrMfMCbFEY/SDZdcKwJVriUe2AjkYgB2x4+Ke8SzQuLTbcdGOxYx4FmgswIhtZ070BYIyK4ms07auPe67DBsoYdWjlYCCtSrd4sWjjIglo0bEF7vncRooOeoIqfhytkigUT/3Xv28e6Mtwuo50HX0AdFz9yCxBoYdMUzmfzRPAvURtlMgRbUuxDjFn5MpQ8e4K/NOnSQ6Lhy5j2ye3PT3ML6NVz1ssnq1HAIRf006nWtmegn739S8Zmoh2tgC/4amk/P0KdEo6wniE9c1P5XPyF33Xyd7DO4vZYP6yp4jBqVyidvGoYGiwX0kWN98Yaaholq677OHo6V72JIGvKXDxVuyh4RqsABbvzc9bKwW34jTWrrUrdvNGmh6+t/NA+kst8diagVQRNwubsi//vWvjTUzFRZoUgglSoMEO7OVG2+80RzGp0Gy9db1OT4Nkq137gGgzjRIWD0h5GL8xCXj9mzFpkFqbSzJ0iAxf8AjgBW3ZdIzwb589913G7dhiMGI6UUgyRo3bpycccYZsnDhQmMtJi7YEoUB9u+66y4TLw3LNqmfGPu7775r3KoB7lhhidvFkkx/U6dONaRZ9M8xTNUAa4S+SAEF4RVWaXIdMw9APzqw48L13MkCTewwgN+mN6IdLNboL54FmvvYdhx3dMkq6Solo/eWdZM/T7gIaucX1rx4ZWc1fY5subuP1cCKmkJNqxwUv+Je/VrGyOJthTJQHQXcNeUYtTQ7OfyCI2TC78dLVX1Vszpb0FjbIGPOO8yeuvskGlg8v1IUuiWVRUvqZVRVnWR3jaSvS9owDSs08lcB7Ttm5t4jj5fQ319MrgVdPPcMxIq5Uh+hF+uSzeDkbd0a2bZxm7zz0NuycPLXsiLvKwmvqpaxlxzpaiZFDQTqGuS1438ivoY60U+eXhUBbhxl6vr1C3ufJxd++RfND9wlxR7Tu5nn+38WeXQfXbBW5am3lnj9knGuksnmuQza7fmT0WReaM+j7EBjwxprN2JY28oCTcocBCCH2NRDuD9TRwwwllIsl5dffrmxiFpQai7QFwCtZYG2rtBYl1NJg4SFFgIorJSA1eOOO85226ax4NJMXK9zAxhaEHr11Veb/rkfsc24iANgEZsvGcZkLN7Mmzy8J54YWRmnHLHW6GuvvdbonLhawCVtibtm7ljmcS8m3hiX8DfeeMNcywug2cYGA7BxY+Z62rOQgZsz8b7WKmvHxTycLNAXXnihuZcF5sRTs/3oRz+KskBfd911xiLN+9ZZ0iChQz5rqwJFBqxtN9RT3Ey+CRdLwB+Jx25W6RYYDcx5Z5r88+cvyIQ1/WRVdb40KOlVQGNXa/VHdXZ5kcyrKJanzr5fKtZGYuBdtSXWQEm/EvnBA2dvr3SsyJuSyPkNz+jfld4tQbvEfadT6ezn/iVr5q2RRVuLzbSD+lkMqfrYs63Rz2hNOE+m/Kwp9V866af1uS7TJrWmmVdzpntOOTNyiXNlSy2/0r1UfLfcra7k+vAsDQqaP4u0c18TaqC+uk6u7f9j+fcfJ8jKWStk0dRF8vSVT8ptB9wqIV1odaVlDYT02e5f592mBkr9bOoifZYnKJkabmM3D+mRNKxh0g81babWu9KyBoL6rPf5SefIjAkjZOHU/rJkeh+ZPam/zLjsdtn25ayWL3Zrd6sGXAC8C9SfiAU62W2te+xAsxosJm4UkAE7MTlwzzzzTGMVxRUYhuJEaZDiWaBTTYNkibAgfjriiCMMAdSf//zn6FAB2qmOBfDsTIHEMePF7RnX5AMPjE0/AmC2TNYAWWJocUmGMMrK/PnzzaFlcWaMWF+deYFpAGC2rtK4RxM3jDs1ccRYdUeOHGn6gcgKUIxg6WXRAN3B8GxTJVGPNRyxY0EHTrHvmS2z9XZPOWDcbs5ye01H3Vdv3ibzpnwt08qLpSqQYQBbUB+SeVBu1IdktqVKgrW+OkM+f3FKR53mLhn35Ift4oxHvirvJhNW95Pxq/vLpLV9ZXlVZCW+saZOZr7y4S4ZT0e+ycpnXpVRSspWmBFUl9KwZBBPrfsuStJ2gJYvf+71jjy9XTL2r556y3yRG0IZMk8/j6urC2VDbb6srcmXxboYU16fK6FAUFZOniG1W7btkjF1pJuEZKkOt+n3yzdytPguv1Y8B6nnwYA9xDN0L/Eef4r4LrtGPBnO2P5q/aQm917oSDrY2WPFq+quMXfqyqsuxDQ0hd5wn/VL18t/nvtgZ9+y0/W3Zd5y2br4Gw1dioBbHmcI42eLPtroD3j5wpWyZcGKTjf/nT2h+bfyeVTlqT5rK7Olqlz/LmqaPWTujbooo+GLrrRPDWS0z2F1rlElYoHmD/mzmsYHARhBoIUFkj1ADVdgZxok4k7jWaDfeustY6EkbhjwSI5dK/Es0NYVGgCaCIBZ8GtZoLGikmMXF14InPbZZ59oSqa2jsWOiT3xzwBUgKZTsKpawAsAxqpKTt5x48aZHMikYsJVGhIrG8ubiLkZvU6ePNnoD2urjfF9/PHHTVwuDNvkZUawdFs2ZvrmWvrH7Zs4ZSzNWPPLyspMGinLAk0dIJnFCCz0pLACPFtBl1i2H374YSGlFezWzzzzjLGqA5bjWaCxSts0TcRjdyQW6DVzVghkQsRZzqsqlFwlwcr1BTRPaFjqQz7Z1uhXi4b+OKg74NKP5loVufs4DfDZWzfvm7hS6JtipbGuURb/52s5+vrvxVa4Z1EN1G6ukOp1myVfwe/+XSt1UUYXYzSeOtOrIJiPokrl8rXSUFkjmQW5kQL3NUYDuEhWrWvyNAip/iob1VqZIOOR158hm+evkL5j9onpwz0pb6YCT8/e4tOtZeFDWqGb61Yer6d6dbffslo/l/F/GLUhMf+THv+3HHXZ0fGXuecODWyevURBWYIvsqMNh6HGgGz6apGUuARjcZqJPa2YPpOH+NjC7WcsMlQvXiIFe+2ZsN4t3L0acAHwTtK/tQKmwgLNwy6g9+mnn252d8Dd/fffH5MG6cgjjzSxrIlYoGGNRrBwAoAZh7U0xrNA2zRIdqxcZ8eSiAUaIi7yCWNlhcQLcN6WsdB/vAByAZ5WsNRC8AVQRCxDMi7GCxYsMKAbpmgE92QsuvHCQgFWa8AjVnLmZ5mbaQuof/nll41bszOvr7Mfm6PZyajNfC3oJaUSY0eISbZpkBjT2LFjzQKEZaemHVZ/XKJx9UYg08IdHmIxO0dToS+4WOMajkCe1ZEkoIAsjLnXiEdqghlmSzSHRn2odiWxBiI6tHpM3MaWEr/qSnIN8HAH6ZqVDD3M0GUYp1AfrFPrnAuAnWqJHgdq68Xr0GG0IsEBvze0dyVeAxEStvjS1M7/m2tTu0NHbFVf0yDejNjFc+c8cI92pWUNQHyFG3RrQpug+5vdmprUkt7SdzUsITWIuNI+NeAC4J30vliLZios0E5rofP29AEJE8RNgE+bBgnQlIwFGsDmBG30AQAknhYQGc8CjTu0BXvcm7EAgpOxQNMX4PfUU081W6pjScYCDUMyoBehb8AiZTxE2TL2WE+nTJli0kLh/o1OsJICyLEM25zBpDf6v//7PwMqTQf6Qr5grOgIoDieBdoCVdNg+wvEV8QLA5AhzUIA6ugHizlWa8sCzbixDgNW6R+LMOOnHLEs0OQgtvpdvnx5dEy2nWmsLxbgc84CQ0eSkoGlKQ+3dHjflNumW0OfPtTlFhdIQ3UrQEKNQz1H9Es39bRpvjk9NCbd4ZGR6GJfpl+yiiOLbonq070sS0mtnIsILepD/3R3GdCzxSbpWQldXXMrcOu6wALseiYk0lNh90LJzs+S2ormOalJxzX4kCGJLnPLHBooGNBLMnKz9ZmyZTd7f06WFJb1clzpHibSQLam56xZQrhDcwmoESV34MDmFW5Ju9BA0zJ5uxhOxx8E5El2A8RhdcUdFhbolStXRicIMMJazEZ7yKIs+CEe1ZkP1pJcxTMvAyQtC3S04+0HsECTExfXZ8i4YFt23t/ZHoAHCzRu17BLAy6Je8Vyijsw4NdKqmNhDlinnRuM2Lg6Y1kF+JJy6PPPPzddU4ZgUQVYwrQMAdcVV1xhxm1dhLHI4uKMMH/SKQFeiZlGz1jQyQMMoEfiWaBhoLZpqGCBxsqMWIIrXKCJe37llVdMnLIF8oBcxo4wtnPPPddYp//whz+Y9yqeBZp7wHSNezS6OPnkk00sMtczx84iPYb2kcLSrq1Ox6dmuFHnjW21XTo3GH3+kUJ6npbFI7RzJbkGvLoIOOycY9WCqcgsiQy74Dtan9ySlOSytCnmN2HQSYcqmWnra+S5uuBQNNhd3Ir/cHiEB98d+4x5pHt8d+65agCvhHPvOz+hLvCiOffexHUJL0jTwt6H7athS86Y88SK8OoiYa8x+yaudEujGii77sfR4/iDnqd/T/xFrT8fxV/nnu8aDbgAeCfr2TJAs3dZoJuzQAMiscBedNFFxu0X8IoAFrGqEucM2RVuzRBj4dr82GOPRUmpuN4yZI8fP95YswGnuD5jGQa4Y421VvFUWaAtwdX1119vSLV666reNddcE/10YFm3IJkxuCzQEdWQA3hs/yVRPSU7GNytXPqVdCz37mRz+bbKj77uVCnsVazWSyxAieXon54q/UcNTlzplhoN4JI2ePmdUlyoOaPiJENj1Ht13ypDG19W1/2Ix0ZcE/d0uwYO/p8LxEdOlFbkmN/8pJUW6VntEayRresvVjs+ZUzYK7bIPYvRQGVFnagntP7OR8gW2cPnVFWv9vb1xE670pIG8H455EplJVdOhGTiUQ6Po382Sr//rQPlZH2kS3nJUUfIiEcjoXkNvixp8KpHY1a29Dr3LBnyi5+nixo65DxdALwL3jaXBfpo486NezFxyoBN2KuxtJKfF8CKJZsUT1ge+vTpY96VuXPnGss48beW+RnwTO5eBAsxx7hwW+Zm3Ls5tymJUmWBxqKLkGfYCn0xNoA5oNeCZMbolHiXZltv97SlH7s5y539dMTjurkzpHvwGzlzxDwzfJ+mVGiSsOYUDEhZ13I5qt9iKf/rY01V7lEzDWTmZcupvxqnrvR8Vpp4NeyxR8nhxv7klGbXuQWxGmic/qZ4/Fly5EELZPQ+S6VHSYUU5NVKaclW2X+v5TLmgEUSXLdYGr94N/ZC9yxGA7klBXL8/stNmVdZtJ2iCZHEr9/tMXuvk2493Ydkp27ssUfdmD1ygJ6mqB/jsBDcfo3txd07NbBizir544+eMcR2lQp4qxUIA3zZQ0Vx36m/lQY3btWpsmbH4WCD9A08KcecNtvU+ZQZ34pPiQMzs9T4cMI8KdnwqMa3tk6WZa9N5/3ajO7y64VD5OVFJfL3JSXyuy9L5ZPAoHRWSYeYe+v+TR1iGu17kC4LdNP7g4sy7M1YbCGDgjEZ92HEWlwBx6QrIkcv5Fh33XWXYVGmDSmUIKFCnn32WcGt+rvf/a5xR8blm5hgYndxsaZvC4RbY4GGyIq4YSzI5OxlbLgvIxdccIHZW0Ksnc0CDZO01QGx3h1J6mZ9LqGaSumeF5KL95slizYXy+ptBdKoaQCKcupkcMkW6VMYiTWqXzBLf1D1Ac91PU36Fn/6lw+UPVvzeGsLr67Cw3YKwzYPd1k+TUfz3mw58JzDkl7vVmgm1Wlv6kskRnBA783C1kxqKqRhxjuSOcpdUGimm+0FDYtmSbY+HH935NeybGOJrC7vIvWNGQp8g9Jdv9ODemzScA6RuumTxd/XfdhLpEevjFb+e+KAFySqjpaFyB2nQDnDd4l+29186VHFxB189vp0k3Ug2BhZaGVx0Cn1NfWydOZyGT5mqLPYPXZoILz+K/1RCUjv/lvlzMumyjfLSmTTugLTolvPSum7x2bJzdPnkHCJhDd8JZ5eox1Xu4fxGmhsCMh1h/2vKV7qiN1/9aF/yTHnj5G+Q1x+hHidtZdzFwDvpHfCWgFdFugmRupkqoXl+u67744yJJOqCCIrS2zFdQBgFg4qKytNSiTbFzG8uCcjuEAjxNjGC9ZlWLMhpCLOGBZo4oWxwmIhRiC74hyyKqyyjzzyiIlLtnl/aYP1GXdtBFCN7GwWaOZk45oZh3ULNzdr5y+NG9ZAJW5GmZfZKPv1Wm+2hMNWl9NwXY148iI/tgnbpHFhozLpLvtwjmoA+xoWjVhPg7rKWpn7z49cANzCZ6Sxqka2Tpsq+SkY3TZNmiC54xqFWDdXmmsgVLFZgvV1kqlWoWG9NpitWSs1EAU2r2tW7BY0acAnx8vE338qh1yUry6lCm/zmx67gpqjq64yJBsW1WtqmqNl6OEu+VWT5pofbVi+SSz4bV7LT1FItqzZEeKxRL11zrJw3RYFt5GVg9z8Bhm6z1qzNZuttgnXJlg8bNYwvQtwCMzOy5K6OALLDE0PuXHVZhcAt+OPh+sCvZPeHCcLNEzQbJAxAWwAWDAp4wKMQICVSJws0NRbFmiOnSzQ5OcFJAEcLQs04A5xskDjHgwL9Jw5c8xYYIBmc7rgWpZiywKNWzEM0owbgieAvWWBtnG1qYwFi+bSpUtjNuJ6EdyJIaeCTAp2ZUAo8b5WYFaGNIxcwJMmTZIXX3wx6vaM9dgKqZ9+9atfxQBnW3feeedJcXGxYdUmxRPzIAfwiSeeKF988YVpdtppp8kdd9xhLzH6tHmDbSEgHOsxEs8CTVkyFmjqLAs0x7BA2/nbxRLKEcZALmA2xtxRJKwkZOX/etP+lrY67BCs2V9Ob7VdOjbgO/Dy8ddLyJDBxZk1tivEq7bg1ZM+k8m3uq7kiT4jpPf41wEnS81Wpxt+opaRsrraDJlyxpWRdEjJm6Vtzep/T5VgK+mNWPta9srElPKKpq0ideKL/pMjd+37hfztxuUy87VNsuzzSpk7qVze+/1aeeLc+fLHM5dIYY++6ayilObec1APychsWkCIv4hnm279SuKL3XOHBjy5EKzFLq46qpsOtYknr7Tp3D1KqIFgwzLp2j3QrG7b5ioND1narNwtaD8aSP6XpP2MsUONBEZnK6xG4qILIzOAjnjX/v37m2qAJwATARgBGEn78/777xuLJgAOZmMkGfMydZYFOh44cU/iZmGBpl9YoIm/TST8aMACTa5a4pUhmcIKyvhhgT7hhBOil6U6FqyyTms4HeDSbOePNRUQjCu0XTywN3EyN2O9ha0ZPSIcA+wBwoBK3KNhr77zzjtN3wBW9EqeXoD7W2+9ZYDoSSedFL3PvHnz5Ouvvzb9DR3a5CpFX7NnzxYssnvttZdMmDDBWIXpCzZqgDsCcP/JT35i2LFZOCBPMu+fZXemHf2jP9yoAfnPPPOMWVjgetuOY6Rv36YHH5isO4qsfuAunaOSvOj/1oQF55C6Ra+57w7JfvpVyewZseK3dl061If1c/L3026VylXrpasyF29L4gIZ0oeWPLXGzXtlkkk7c8CPI6EA6aCjVOb41Z0PiTcrUzZu6CJFXauUFR47emIJBj1KvFco1TVrZNlf35DBl52TuGGali599hVZ+JdJMnygPgW38P1mLXfL+pDMe+APMuLO69NUW61P+9CLxsrCj+bJZ3/daLb4K7r2KZYeg1xXyXi9xJ+POftgef3BdySgbqeJhDzBA/cvS1Tllm3XgKfHvtJYpzH8rWikviogmd33bqVVelcDfuu23Cg3/T+v/M8pmjrOq1ZzDWFCLrq1TgoL/iT12wKSVXh6eiuqnc4+sSmynQ62IwzLZYF+x7xNWHRJ7eTcLICcPn26XHzxxYYF2lrFne9tqszNuDVjjb722msFvcPSzMIBgJJFBVIRsRELTP5iLNtsFvxyT5sSCbD72WefmbRVBx98sCG+OuusswzQ5nrif10W6KZ3qf6blVL50ftq+QkoCPa0agXGTagx4NN44RrZ+Pyfmjpyj2T5pOmyZdEqCQWCkpsRkl7ZyupiJGIJVu1qTHBYBufViE/1GNL4t+mP/l1qt2xztbddA9Ur18ja9z6RkFqBN20u0PCG7KSfSRZjGhoyZMuWfAnV1cuCPzwvtWs3uLrcroGG8gpZ/NjzeuaRb9YXJ9UjzdfqIkJDvVdWvzFBtkzX2EJXEmpg8JhhUl9Vl6Au8h0fdOAgk+InQQO3yKGBHgO7y/JtTUSVtkphhzkcesZ+4m/BQmzbp/N+7kvvyX/+1foC9EeTButi6/vprKoW5x4KrJeq9deZNoP2DcmT6tVxwkUNcvTZDfKLF6rljGsaTF3dtr9IY+2nLfblVu4eDbgAeBfo3WWBbmKBRt3vvvuuAZbE6UJyFS+pMjfbmNx4C7Jlci4tLRXb19tvv23y8LK3lnUs0j/72c/M7QHTiK0zJ/qCFRerLHHHLgu01YpI5QeTFGwA1Dz6AOxXt/poWFFTI8dRfX2GrowqjAs06rX/1uPk1jnHZWlx+NXTb0tjVW10rl38QRmYWyPdNKa6q79Rumc2yB4KfjN0ddlKqDEgi9/+2J6m/X7dZAW/UQI5j3w1q0wXwXwa+tC0OAPwbWz0qrdGrnw+bYjqLLJSjy7XTXEfUOyHaN3ED3SRJRKuUqULCYtXlppFrkDAY9LNBFSn6BXwu1GJsZBAVbUsf+Hvtgt379BASHP0PHLyPRFSOwPU+B7bjU9hWOZPmSOz353puMo9TKSBB657Wmo0fc8aqZdapRdTVgkJ6FalRyulTt5/e5pMeXNaokvdMtVASEPBpj/yiixbVCofTBxudOLMBsdxfb1P/vPvYbJsfheZ9vAr7m91kk9OfeXrWtPkRFvSMyw/uqdern2oTvY70hGGE66Xuq0sKLrS3jTQ9O61t5F1ovG4LNCxbya5dkktlExSZW4eN26cfPLJJ8a9nLhoXIuJu4Y4CzdpGKPj5dNPPxUszAig1sb84qaMK/g999xjmKOxDAN+YYOmb0AwbM3IzmaBNp12sJfKzz9SNBFZ4eQRrrYmU3UUEJ9aMJ0SViKnRrW2hRT8NolH6pcvkew9ACHpLTyQrP9iYTMlZKkrdJYvAkKaVWpBAMKs8Z/JPpeclKg67co2fDjdWH/txPncTf10mMbUaxxWt22Spak96ur8snlToWwpV+ri7eCX9liBN3w0TQaef5q9PK336yd9KKHaJmtlbX2mfL2kt+TlNCghVkBT0HjVdTxLwXCsb/SWz10LcKIPztr530jFukimg2QpvmsrauSjP78v+5x0QKIu3LLtGpjwyscK4pQ4TM/Xiv39aVJPYFutvPbUJDnqtAObCt2jqAbKF38jjcqUjSya10s2bSiQIXuule7K/sxy4AZlg144t7ds3ZJn2jRW10r5ktVSPKSfOXdfmjQQseomdsVvahU5CgW3qIfXBvFm9Iivcs93owZcALyTlQ8xk5VaJf0BbAGASe1DHKoVSG+efvppexrdkyYIlmSsmsT3blNSnCOPPNLEy8JqbAEbqX+I27300kvNtdzXGeMLmMO6ST1EUcTF3nrrrcZFGGDnFMYCUZR1DSaVEC7MxMQS+4ob8+WXX77DY3Hei+OWwK9t++WXXxrmZmKqE7lJ0460ReiKmF1SFyHEVrPde++95jz+BYCclZUVtebaehihsRgT04s+nAKYRqzF2ckCzVyOOOIIAViTo9i2w+oPkL/66qtNGTHFp5xyinHFBlRbJmsqGfsHH3xg2mFlZhztXQKbNsYNUYFuo183ddfdbqlEjQARJ9iwFwW2KLukC4ClYZuyYid7KrbKSrKv3rAlSU36FddtjHz3YmfuUTfnArPFljc/q1uf6Prm7dKhpG5Dc12Ew17BGtySBDVUBGu61+8+Vjj1tGHxuma/Kc56e7x+UYTnwp67+1gNbFxbrgupsb/NsS0iZ0vnfpOo2C1TDVSvi/3NKN+cL59/lHwhmufI6rWbXACc4NMTDkXSOyaoalbk8Wj4V3CzC4CbaWb3Fri/VDtJ/wMHDjTWwoceeijaIyCW1D4wDwMkLZAjRhUL49lnnx1tCyglzhTWY0igfv7zn0dZoIlfJdaV1EDz5883jMYwRD/66KNRFmjbUVlZmemH/iwLNGMC+BEfC7AlBtcKY6FPywIN4RNxsqNHjzYbAJQ/ghBknXrqqQbApTIWSKG2bIn9YwtztZPkCUst4413YYZZGUvs+eefb+oB5oBdgHl82wMPPNAQTRG/C+EUc4EQa8iQ5n/U586dK9OmTZNzzjnHxA1bHbAH3K5bt84AfRYciCHmvcP6i57Rt2WBhlwM0ArohYyL2GbIxiy7M+0CypDM+7hmzRrzfhxwwAHGBZt72XYcIwDjffbZxxw/8cQTZt/eX3y5kRXi5uPUFD7bSSCa10VKiNfy6eKAK5r5Mz9H03qktoocry9/XnMPh/g26XLu/y8/T/58N/2M/axk5O2gLnStywW/VotN+8zcrJTiezNzM5suco+aaSA3P1uCypPQmuRoShpXEmsgU39vWJJOVVhucH9nEmvL4/HrwlbEmp64RVMpzzwej/t73aSR9nHkAuCd9D5Yt1pndwAdQBUADoBpSZQAv4A8YmCdgsVw5MiRSVmgAdHxEs8CDTMyzMqAWIBePAs0brxYO60wFgCukwV61qxZxoIKYzOgEeswFkzEskC3NpaWWKBxISZtE0AcHXF/2K+POeYYcw+AI4CUsZ588smmzL7AwDxixAgDPCkDFMNYzZwRdGhdqE2BvnAPADWLCwiLAbBKw95MqiIEwI5glU9kmYd9GhCO0A9W5/Xr15tzFguw1Ft2ZwAxTNo333xzdI64Y1sLvW1nLtYXFjaskPKpI0hm/zKpnU/O2h0QfT8yekSs6jtwdae6xKfWspziQqla09zq1uJE9Smm215lLTZJp8q8sr6yefqsHZuy/v3JH9h/x67thFcVDB8s5V/odzvOE6a1qeb07tlak7SsLxs1SN0fY72u4hXhU+KmvY4bGV/snjs0kFeQI333KJWFs1Y4SmMPIcA67KT9Ywvds6gGiof2b9OCa6ihUYqHD4he7x40acDrH6B50iPZRJpKkxyFQ7o46LqRJ9HObit2BubttkF0phuT+sdujzzyiHE7BlxhlVy5cmV0qrjp2nZYWXEzhn0Y4ITYPSBwwYIFcvrppws5ert27WosjoAucvxiabSgNNq5HpAGadWqVQZc4gqN5RS34Hj3Xq5hLFh4EVx6b7nlFhNHC3DHEo3lF2nLWFpigQY8bty4UZ5//nk56qijjGX87rvvNvmKuQ+LCSwQYGGNF4C6BYnMHfduXM0ZNyAX6+xtt91mFhzstePHjzcAmzboDT0AnGlvhXRIWKeZO/pi49hafcvKyqILGBMnTjSLC+xxLV+yZIl5H7BwIwBgQD4bc2QxACD/3nvvmbnyHnZ0KRx7rHjzC3ZoGn5NgeQviaSU2qEOOtlFA48Yqt/Blh+Q46ecqURZexw/Or44bc97HX+4+HJ3bIU9Q6/reeyYtNVd/MR7Hj9W/IX58cUtnnuzs6TXSZEFzBYbpmFlXnG+HKJpkHSdJakElU3/6KtPSFrvVkQ0cP2DF7eoikbV48U3ntZim3SuzCzIlQHHjhKPr/VHf9oMOO5AyXQ9jRJ+ZLLy9bnYk4q3nAEupAAAQABJREFUjE/8uXz/YzkTEnbqFu5SDbT+Ldilw+n4NyMnrd1we8W9FUBGKp2PPvooOkGsnrYdKXxwscWSaa2XuFQjgFvaXnTRRcaSPHbsWAOmyFcLqCK2GKuoM66X9oBawCHlPXv2lGuuucZYc63VMjqQuAPGCTsyfSC4ZVtpy1iwHjMn54YL+Mcff2xy+RIbSxtAJ4CQeWC1RmBuJhaacZPHF+Zm0iZZscfUMR8sqk899ZSJlWaegPwPP/zQNAckP/fccwYY43K93377CToktzFWXcvsfNpppxm3Z2Jz0SsbVl3rCg24JXaYObDHuo5Vl3EedNBB5l59+vQxe4A2eseizRw5J28w7wnHVrd2Ph1xX3DEseLLbPufD4+SO3W/7Mcdccrf2pj37LtAMnzBNvQfloJ8jRXPW9GGazp309KxBynD+I65kkNE1n3MqM6toDbMruSg/SS3P3/LcIBMTXzZful/3vdSa5yOrVavkmxfokWuSIqzsu5eqdsc8UJKR/WkOufAig2yZ3aGae5cT+CXyK8Fh/cokNr1rh5b0ueht42TzBRCPsJKNnb4Ly5vqau0rvPnHiYZWXhG+lvRQ1Byuo5rpY1bvTs00PYn2N0xyg5+z0RpkABIr7zySnQjfvX22283rr+kBgIcA76wMgKksebitnvYYYfJsccea4AbuWsh1sLN2mktxdp71VVXCSD51VdfNdrDFRrrZ3z8qVO1gF/cnefMmWMs0rj8YsGcOXPmDo/F2T/HuD0DHInddQoWYzsHFgGIXwaw4qLcpUsXAzpte8AyYsE8ABTAiz7RI7rF5Zz5QBqGxRgCMcQuLGCNB8haUjFAMfNlEYJxsGFFB0xfcskl5loAMe7j9EusLqRXtIOJGrGLBZQDclkwQJeME5dvADB9dAbx6FxK9s5SAqdED3VJZugJ60poWPLKXOuvU0M56z6UQ/ZbrJ+ZVHQJKPHIsQfPkuCcd53dpPVxUF31wh5Ns5U6ZovqS73TFDy3ZQEiemmnPPDo78fIm7+njO4oMxWFhmXUtaMlq6SoU+rjv53UttWbZNWnc006s26a0izbqyEg+l3361aQEZSemvc7rF5MM58Z/9/eqlNfz+/6pEfekW5+n4zK9Usfv1e6+jxSpNuATJ8coDHU3vpGmfJHV48tfRAK+/aQ8yf/wTTxZTUHb77sTMktLZZLPn9a8nTvSnIN5Hb7hRoChkpDUxbDaOO6Sv569paC3n/V56RULMXRS92DXaSByFLaLrpZut4mURokgBWxrlYAhbjbApJuuummGBZorJVYQbGUAvYAbZAvYfl94IEHjIUzngUal2mYhQFxuAVjiUzGAs0YLPiFfRnLMYAU4B7PAr0jY7FzZE/MMSmH4smscBsm7heLLMCU8UNsRZwuVnRAOEK5dSHGtRnBxThesLYDVgHQ6BV3ZCuAV1ygcSG34wCw4rJ+3333GT3xY8t7AQmWBc8W4LLoAFkYwBqLLizQLFRY0i/mCEiHzdnJAs3CBYsWdo7O8bBYgeAu3xFYoBlrVsYmKSkrl01LSzhtUQDKGVkB6bm3pq6ogO10vxbbp0tlWH852cr61GqKmbkyaeoIBcJKmaGsu/GClThLcwOfcPhsyVZdhjYtj2+Stuf1G7fog0imSWmUqhIsWPZl+qVerW85vbqnemmnb5ft2SCHnLpIpr452Mw1FGzuvsd3mrRnex64QvKkf6fXyY5OcMPXy81vCdfnqr7Y4iWs7MZrZiyIL3bPHRqoq6iR2q3VpiRX3XMHJnDjRY/LPl/kuMo9TKSBvB5FctlXL8gXT7wuH/z+H5KjPzc4/dWJV8Zefprsf9X3JbuoMNGlbplDAzw3btt8jfzztttlnxMrpfeIoDI9i5R/45WvJ+bIHmNOl8N+sGOhYo7buIffkgZcALyTFItlEesecb1WcL8l1hXrJIDIpkGyzMu2HXviWNlIW2TJoHAf5losocTNAsSIj8WKa9MgYV207r70U1YWYYEGYMenQcKlOBELNBbkRGmQcAeOZ4Fuy1gYT7wAcrHoWgHsL126NJqb16YIgujqyiuvNAAfV2UEHQNarQD60TGs1IB7wDuCJR3dAaSt4OKMbnCRRoYPHy6UOYVxAYABqSwCAF5hzrbC2BHeH+6H9deyQAPWnWmQiouLm7FAYxGmbztH2y9A3VqGAd4dRvQzltulXnoM3iRbVnbVz6Va4IL6K6oWyqiolUMNc5LbtVaK+1ZoVaGxqkfr0/5ALWy60IL0Lt0q3ztuhsxe0E++WVeseiKUIaweDR7J1BzLQ8vWybA91uqxa61s9rHx6GeOTT97/O2zYorsyfa9o1pLPHoZ18Y1SvPTsJrF/f6QjDn5a1mzrETW6tageZQ9+nkM6+cR4Nutd4X0H7ZesnM1X3WsUtNce7HTTzXNmavCWL01O0vxe5roO9+sL7dAVn2xVGZOmCsvb/RIvS4c8CdQf20kb/wcKTloXxl6rLtIncrHBFfxVV9ly/zJsSE4/pxMGXBg84XsVPp02+waDbgAeCfpGWCKy/Jf//rXZj0SowqYs2mQsBAmEqyRxKsCmiGfsgLrMCAPF+bLLrvMFBMniyUYEEgMsZXly2NZoAGMzjRIiVigIZyKT4NEbCvplwCVThboVMeSLA2STRHEeAHyWHixrJLyyJaxf/jhh0387w9+8AMTr3vHHXcYEq5x48aZtESkKEKIG4aIyimQXMHkbN2/bd0ee+xhXLvJb4y19sILL5THH3/cxBrTBvdsADD6R9544w0hrhcLL2IJsbAux7NAA1ytezntWMyIZ4HGAozYduZEX2644QZ7aOKYoyft/CCcxULGaskuaJBee22Q2opsszXWZxgg7FOgll1Yr+BXXdEzI6AtVFcv3i492/nMduHw/Erc5GtyQ+uSXyeHjyIXtUhNXaZ+7zN0IadRcrMVZMSJt7hvXEn6nmYV6wJMvXoXGIk8ynHYMqiIoF7cpzOLmhblIn2k9+v/Z+88AOSqqv9/pm3fzWY3hTSy6YUEMPREeglFkA6CINKUn3SUn2IhUgQFld8fRXoQUSmKIB0JJIaWhJBCSIP0ZNOzvc3MzvzP507u7pvZmS0hkN2dd5I3771777vv3jOzM+97zznf4y0eqqFt2eJvbJC9R241WyjoUxDsl4B6H2RkNi/CRNXk4ek3Pr0V1srsi0cO0kWD5kWZpE31o9hvv2FJq9zCmAayCrIlMzdL6iuT+JtaJakeB31NP7uutKqBDx95Xd666xnBqv713AyZVtUg+HgcmZ8pmxatkSfPvUtOvuNimXjVKa3241aK7DVmoOT0zJaaHdVx6gjVBWXs8fvFlbknnUsDLgDeze8HFlMrgCLidnFDvueee4zVEVdkBPdaC9wAzgDG6dOnG7ZiXHqxPA4ZMsS0xXKIwEps5cYbbzSHiWmQbL11fU5Mg2TrnXssIM40SDBQEwvL+LGm4vZspb1jSZUGCVdn4mKxpgLiZ8+ebUCmtYCyWAB4xL37+OOPN4D/+eefN+CXMQCW33nnHUNCxTmAHss2wNjK3//+d+PyjHXYxvhSt2TJEgNcsRBhjWexATfx8847z+TrxapbUlIiFRUVxlKOqzZ6eOCBBwSWaMaOMDZIrWDHZuEAEM/7Z9Mb0Q4GbfRHDDX9PP74400kX7ad6ayLvqDDqtW1or+dmuNSDWj64AHQZWtNoprztra0UvIHt9Yqfer42/MMPkQii9+IGTB3Th195mYHzZZMG42RgGSNPzlZVVqW+XKypCbkk2YeaFVgnABAEstiDWoaA+LL1A+yK00aiPY/SOrKapSvoalIArqIxZYo4QZdaOhzuLgaTNRM7LxgoP5uNLKAlfozSNWAA4Yn78AtNRrgmWnSd4+Vl3/1D3XUTSGqx69femyKSrcYDXz89xny8o+nNimjp8ZUn13UMkb11Z89qSn68uVr5x3R1NY9aKmBiKyXS572yz0HiS74xxZdWXg9/6E8yes/Vy9wf6dbaq1zlKT8Hukcw+t6o8Aaa7cDDjigwyzQgCUkMQ0S7s/UEQOMCy7A7rLLLkuaBomHan4scOO1rtBYl9uTBglWZlyBsVIC1km9ZMWmQWrPWFKlQQIc4ioMqzWM0DZ1EFZX4mmJ7126dKmxptPHmjVrDADFJRrheqsbAC0WanInW+Zm9uTVBUQDlrFuE4NNGioWDSxTM27oWGrJc4xgSbZWdvYsQFx//fWCpRkCK8TmcWYMThZoLMno2TJ4E0/NduWVVzaxQF977bWG/Iv3zcYwm0676Ev1++9I9Ra19KqbbnsFsqGqLTmybWq8xb6913fXdqVlI/TvjfX39ks4rLFHUdfSYTW29K0FsqEhX8LqnptckpfTfn19nnw2Y1Hyy9K0dMW/31MSxZFtzr5RQx42b+ohHz3SMmVdmxenSYP5D/5LMj24R/IZ1CfjJJKpxFjz/98zEqyuTVLrFqEBUhw99OvXpCbe07RJOYCOjSGvfDTTjQFuUkrCQbCmXt6Y8lRCaerT13/xFwnW6gqXK0k10Ni4QWrq7pH8fg3y8896yln/L0++cUeuXPN2oQw/MkPqg69KQ3Bm0mvdwj2vARcAfwXvQTIW6FS3te6xTusvgJb0PWVlZXLWWWcZayfWTVyqiS1OTIME2HWyQLc3DZIlwgJ04vbLfurU5pVCrL/tHQvg2ZkCiWPGO3HiREMCxbjpm1hcwCOWbMA9/VuQCjglPzKWYIi8EMAzDNEIxGDEQANIEcaPYEkHyBPDDJilD5iaiTfGWotguaWupKTEnOPCDtilD67F4ovADk0eZmKfbcokxugU+57ZMltv95QzR7s5y+01XW1f9e40aayokbJ1here1/boadMY9kr1tlwJblgr4fKyti9Kkxar3/lcNm4ghjr+c9Xa9D9dNEhK357dWpO0qlv82lzZWK5eCWEWZdo3dbxSq7U9WVMWvx4j2Wvfld2/1dK/vCorlhbJ3DlDUk42qC7Rm0p7ysx3xsra/8yScL11QU95SdpVRNULaOHjLyvRXUjy/OqRpAR3MRAc2ytrgqZHapQMTQ8Xqq2X1f+Zk3Y6au+E5/4nRhS5vs4jG/Wnvl6dEfgbbtStVkHxWvWMLq+Pyl/vfEEqE9xR23uP7t7u8+kLJawLCe0V8lOv0GtcSa6BuoaXmyoC2R4ZdVyG7HdWpvTo3wytAMHRaEvPmaYL3YM9poHmd2mPDaH73zgZCzTusU888YTZAIIwEF9wwQWGDIl0PIlpkIg7tSzQgGPIl8iDmywNEhqFLZn0SbhCk/KnrTRIFvxiUQUw4vqbKg1SR8fifIeJLWbsgHnIoLgv7sNYbC1BFeAYK+5fNR8xAJf4YAAqAlv2mWeeaY6ZH4K7NamKAMrE2QJuIRMDyEJGhRUbN3MArI3v5T3BooueENiiGQOEXIB3yw6NCzsC8MaijMAo7UyDROolLO5WLIgmjtmmQYKtG6DMZoG0bY8+n332WbNBiNUVpGH1SjPM+qos2bYq5rWQCghHFNg11GTIpqV99BoFeeozHdoU02VXmOuXOcaofh7qt2yX5csGyvZt+a2CN4BdSC0cC+aVSNm2PCn7ZNmXObQu1ffm5fp5Um+ETyt66gOxxzwYtzYBLL81Cn4/qVCyMX2K3rR0fWvN066ufEXs73P5koHy9pvjZOvmAv3s+fS7y2/25WU5Mu+jIfLfd2KeOV51o6xYsS7t9NTWhGs2lxlgSzvWTbOVPCxPUx/l6mb2ypMQUGIxJFhVKxs+iIE8U+C+xGlgwYwlUq0xq0h5yCOraj2yrNojy3Vbo6C4ducCos/vk2VzYr9PcR24J7J+7ufSUNn+Z4x6bbv+4xWu5lJooLGxHbrRB6NIJPYcmaIbt3gPacC/h+7b7W5rrYDtYYEG/GINhKgpUQCId999d1wapCOPPNIwCidjgQboITYNEuOwlsZEFmibBsmOlevsWJKxQEPEhSUZqykkXsTpdmQs9J9MmN+UKVOaUgQBVAHdltiKawDAgFQsr7gYW4F8CssuAmBFcHMGUCNYxhEnkCR2GuszMdfoBiEOFxdx644MKAeIAp6xRBOjzHxtjl/GgcUYAYDbNEj0g5UYRmgnCzRWf1yibRqkMWPGGHd4iMUSWaBJp0RcM9JVrMOR6iozXl4aarJk07Lekt9H8zbnN5gHPdTMA19YrURVWzSuWwmyDPjlVSsaqyu5NO0lWFktXk3DE1ECp08WDJH+A7bJkGGbxacPxV6fMmirxSii7NqsG9RUZ8lny/prjHqu0Vu4qibt9WcVYNOjwGX6wfbeMjyvSnpn1utZVHVpW6m1SD0RaLOlIUtWVjenp6hTbwZXYhowltyd35OUbN7U02xeTXuUpWRsELOFwwku+/o3Hax0dZj4GQrq36jX+QHUBnwv6v+kUl/e/L2atEEaF1Zuq44Zz9vQAc80tVWtEGW1cX13rq7Z0fHPV81297c61WciKu2zpkejrht5Kh3uyXIXAO8m7VuLYXtYoJ3WQuft6WPhwoUG7AI+bRokQBPgKxkLNCmQnGmQ6AOAS0wrcaqJLNC4Q5eWljbdlrHwgwHwg8ALYMe1lgWavgC/ED6xtXcsqVigAbvcD4urvbdNZWQHBaCFNAw3ZNyPnUKcsBUswBdddJFxhbYAmHrA7jPPPGOAc0FBLO0OgNiCX66H6ArrrnVPh7QKAfgCRi0gNYX6gs4s8EYn3AcAzlixCNO3XViwLNDOOcLOjUUdse3Mib6QDxqdIM6Ya1PQSV/8xb0luG5V0+jCahkqW4clWIGbWjlI34PLczTiQB9NrUX8RTG3dUdRWh5mFOQpY3bsvUcBpRt6ycbSYsnLr9NFGmXP1vRHoWBAvSRypL4uM05HWb2L4s7T+aSgX5GULlq7UwUe+by6QNbW5kpRRoPk+0OSqYsJDfpZrAwFpCyYKcFIPIDrode7EtOAL1NJwXRRJqwuuU6JqP5qa+M/g031akXP7uPqsEkfOw9yVCeNSvzXHvHob3FhSWxxtz3t063NwFH9NJuALr604cLLc0Wfvd3fl2Sfj55799bfZ33mCzf/5iRrZ8u8ak3nGleSa8DjydVnv9ZDP/RJSJ+HipN34JbuUQ24AHg3q99lgd5h3I5TsUDjXowVF1dkXIcfeeQRmTNnjrEI4/qM6zeAEACN+zMuzcT/0t/HH39sUgvdcccdgkWV9tOmTTPAEvdpgC/9UQYgxZ2Z2GkYrokLvuaaawQyLazaAGNSK8EETcwxTNDcByswaaoAuMQnk7cXl2UsxDavcEdYoJljWyzQTqbqrmIBzj3gUKldpHGTOy3uzX9G6n6q1qHWfl6jkUbJ3Hto8yVpfOTRBavCfUbI9jkLm7QAsVhVZY7ZmgoTDngw6Xv0oQml6Xs67OtjDZEVMWtWALmb6nNkky1IsfdnBWT4ETFX3hRN0qqY76A+o3rJulmVOu9Utsp4lXgj9dJjiAve4rWioTWFeVI0YpBsnrc8sarFeSAvWwYfd1CLcrcgpoGJp02Qp+/+t1Q7/saT6SZT86+OnFCSrCrty4YePk7ee+AVZXhXa3o7JFM/k1zjSnINZPgPkoYQ3nst0xTaK3zevgqAC+2pu+9EGkhunulEA+xqQ7EM0OxdFujbjGsz7s1skFXB/PzBBx8Y12DAMBZh3JABs1ieEeJ9bZwsYBbAC/hFsLZa13HAJZZZgO3w4cONhdbGEdMWwEo/5C4G3GIRh+EaIV6YOsi3EKzruDaTmol4XO6BxdeSjZWUlLgs0EZTsZf8w48Vb5YjR4qjrtVDBXx5hx0pHmXDdiWmgQFHH2BcnTukD11E6H/cxA5d0p0b73PiBMnQB99dEb9aPMec8LVdubRbXhNtDMvQnLniV3Km9ghu5vsMXSuR7evb0zzt2hx2yyXiz2r9s8mCVo+SvaTfgaPTTj/tnfDgMQPk6PMPa+FSnnj9zU98T4gDdqWlBgZqqq2iwVh0NUapTYlK8dC+MmBCzHOtzeZp2CArc7L+dhfozFNDqdzs76WhZrrGlFO/a11j/F1ilNbN1ul6nGrg1j12yJAhpklHmJdtn7hBd1YW6I8++sgQWR10UPxKNymPiKNFAKGWNRqXbNiecXW2ApszYgmtnK7N6Jhz3JABrbgWkwLp8ssvt5ebvTNlEQUA4dNOO81YnTkHhD/66KMGRJN2CquIBeWJVlr7nnEdYuvtnjLGZDdnOXVdUTJLhkvugQrAPB37CgH49v7O/3TFKX9pY/ZvXalEOICN9jyUxIZRkNcooVVuug/7pvQdNUBGHD1eXXc7trDiU/ImwHOvIX1tV2m/Dy37UHrmlEmJxqN7Pa35cqAqdX3ObpD+xVul/r3YAmbaKzBBAQMOGyfH/O76hNLmU392puQP7CPnvPxb/Trt2Pdpcy/pcXT9ny6Vw886SPUU75ngU7fe3MIcueWv/yMTjnUtlqk+DbiHn/2T4ZpGk7/r1n5vqPPIBbePMaFqqfpL93KPJ1MKcn+h35P9pV6N6g01UQnWRaVmhxJfhYskP+enqj8AsiudUQMde1rojDPoAmNqjQWa4QOMIFyaNWtWu1igcZl1skDjxgt4JMWQFdiNZ8yYYVigsX7CAo0bMAA0GQBzskBDDnXIIYfIFVdcIU8++aRhkx4/fry8+eab5tjJAt2esdgxsce9GJdgGzNt67AOE38LyASc4pI8b9484y79jW98Q2yMLvOyaZCY02uvvWba0H7Hjh3yxBNPmC7JMwxjNAKATRTYrhEs9cihhx5qxgQbN/OeMmWKOcdV+uqrrzZtElmgSUlFbuTWWKC5HnKvxx9/3MT+ApaZo3WnpmPGbcF1Ipg2N+6kL/1uvl0qpr8lZPbQ9YE2RT/mUnDmpZIxcHCbbdOpQfXHH0nfgipZVxaLoebBI7VEJT+zQQoDtVI1d470+PoRqZumWc05910uaz9cIuWbKnTmrenQKkajs0KNctbvLrMF7l41AACO1lbKPsMqlEAsIsvX7KW/UegzXqd+X1iKC6vl4HErBZwcWvi2yOk3ujpMooERp31dsnrmyXu3T5Wq9Vsk2BCWDHW953tz9DnHyiE3f9sFv0n0lqzoF89cKwtnLpU3pv5X5k5bJIW98mXCcePkpMuOkkEj+yW7xC1zaKCw/h257rrpyvminlj64x1UjgmnZGSE9NkppJk1Zkv2djwXmo0PznbucUwDHo9PHrzQIzs21sjAfTwaYy1KCtoodeWZctdctba7zgid9qPiAuDd9NZY4OKyQDczUidTLSDXuiFTbwmwAIqIZUgmVhfyLdIH4TJthTIrLALcfvvtQrohrLxWSA1FSqlEYZEBsi+EPiGcIkYYwWIM8RhgFmZtFiUGDRokt956qwHktGHsyO5mgb7llluaSLdYnLBkW+ZmnfjFl5MrlQ09pCCww4wyFQgG+LLVhbOlsAD3K1ecGmjUzyWsz3sX7ZCtVXnSoOl5IgmAAzdTHlbys+rVOhcjJwpt2ezsJu2PczTe8orfnCz3XPy0sVxGoqmtaX5Po+Qowdj3pl4umbkwlLtiNRDZXqqH+gerMnrIJunXu0JWrOsjW8vyJaTx/X4lFCvMr5GhA7dKn6JmVtlI5XZzjfuSXAODDt9fzn/z/+TdZ96Vey+6X8KaHuXF2qfEF3Afw5JrLHXpvoePlr2G9JaXpk6X0rXb5MG5d6Zu7NbEa6BqvT7TBOWmm96WBQsGaKrGfoZkkUaFhbUyfnypkqCW6jOReiVpW1fa1kDp0o2ybU1I1jdTeUiWZiKoq6yT/OJm4ta2e3JbfJUacL95d5O2rUXTZYGeaTSaigUaoGlTFbFoAKikDMCJ2IUEQC15jr/73e/K5MmTDWEV1tbLLrtM7r///qaUSViUIctyClZhrMfPPfecKcYNmmtwp7b3IfYYq7FTYNrGymvbrFu3zlxzww03GGsy40QY4+5kgYap+5xzzjF9//SnPzX7rvICo2R5VY5kZoQlKzOkNqLY+9g0frVwhDV/aF2DvscK4HyBhPqmhul74FPCtcbKCgVtopbgagXAPqnVVfmgAuFGZd71k34mEJLczKABylZTAfUscKVZA1H9O99w/x/k/FFbZcHW3vJ5ORZ1tUwaJnJVrn42M1SXfAJH9iyT8b23yNa/PiV9TzrRtb6hqJ3i7bmXPTT7Hnl1MmHMmriyZCfevJi+k9W5Zc0aOOzsiTLk0f/Kpb882wW/zWrp8FGfgcVy1rWTZewhwzt8bVpfkB1jJAbgHnTQWrOl1EdWUcoqt6JZA70H5sj2tbGFfltaX12/y7wUtg93/+VqwAXAu1m/Lgt06yzQuDovWrTIAF9ifWfPnm1YmLHOIuTVJb0QTMsQVV166aXy/PPPG1dj6gGopCiC0AoBFJPa6ZJLLjHnvPz97383ZFkAbVy0bWqnSZMmGcsvhFm4Hf/qV78yLtPExZCmCGsv7NKAd/IdY2EmdzBgmTzJjB3Z3SzQpJyyAllYV5HgnGclO2OrVIlaLRWwNWh+UJO/VkEG1uBIxCNhctjudJ30KqlOYP4fJHr6mUqCFVtM6Cpz/TLHmTNqjAQ3NK+0Z/qVJVu31sSrjOc5Y1zmYqeOVv/+/0mopl6y1EJ5UL/Nsm/vbbK5NkfKGzKlRtMf5eoiQk91H++bq4tu2oY1t5o1G2TtAw/J4KuvcnaV1sf+kvEi2eqRUxf7Tm6XMpQLwD/8wHY1TedG7/75HZnxyFsiq7fKU9/5fzLm2PFyxhQlZ9zLXTzo6OeChe1Lfn6mBDLVY0aP+R13pW0NeAYfL9FS9aoLxTzaUl4RyBVPyQkpq92KmAaCK38r371mutz83lAlX9PY30Y1A6gH13W/Wi3RT06T6MGv6POQC7U64+fF/cbYze+KywL9itEopFaW/dnuAZBs27ZtE2J0YYTGzRiB0AqrKozQS5culVAoJPSxZs0aeeCBB0z6Itpx/YIFCziUJUuWCLG8++23n5xyyilN2wknnGBAtI3ZJWYZF2vud/LJJ8tDDz1k3JghusKCjMAUjQWY3MLcG4B9/vnnm1hoYrixChcXx1ZOGQP1xBgDXi+88EJT7yTWIqb6yiuvFJiumde1115r2gPImWNXl2g4KMGXfim9B6rlsokt1mMsliG1XAZDmq+xkeAXLG+IkuXkN4g/uE5C81+MFbmvRgNFJ31DvHkdc5Py+PxScMhhrgZ3aiC4fbtsefHfEqlrUA8TnwG3LCLsrbHVAOHD+m80+0F6bsEv7ryR2jrZ9Ow/JKSLXq7ENBAYe3jHLeJZeZJ52BmuClvRwLP/+xd55odPytr5q6W2vEbKN5bJrL+/KzcPv1q2rtrSypVuVaIGPnxlnlw0/Ea5cNgNcna/H8h3Rv9IqlWnrrStAd9oNR4EctpuqADZN/qcttulcYvQOuV2WU+qy3K5/4X5cuK5m+WEszfLrX9aIuMO2CHR+vUSXHileT5MYzV12qm7APgreGtcFuijm1idIeqC8Im0R1OnTjW5dgGXsDAffPDBulLmMRZh3pbFixcbEI0lGCIvBPBsSbBgh8b9GUCKADqR6dOnG6utjaWlHAszoPt///d/jVW4qqrK3MvGHh9++OHyi1/8QioqKsy1I0eONH1B2IUlGbdnS1SVSCJm3bbNBfpi6+2ecuZoN2e5vaar7Rs/+69Eg7WSV1gnhX3QJaySqQSnU48MHrNJPA1VEprxYKqGaVlecNgkyRw4KNF5vFVd9DrrHPEXulYjq6SyGTMlGorlAGbxBcHCm0woZwvZdpr2p2zme8mapmWZNztP88Kdp9957Xs8MLrMGyyBYV9LS321Z9Jv/t8rMu2PrytDbDCueaQx9r157+Tb1OBeG1fnniTXwJJZn8st37hXNq7aKrUaY1lf0yAbPtskp/W8Urasc+PQk2utudSjLtCho/7SXJDkKBz2SuiMD8TjukAn0U6sKNqwVcKr/6g/JrG/6azsiJz+nY1y9uWlMnBo3c7rohKp/lQi26al7Met2HMaaN8v3J4bX7e4c2ss0E8oazFAEPZhiJsAasOGDZNx48YZS2Qy5uUhQ4bEsUBv2rSpKYWQVRhsyVgzH374YVm7dq1hgSbXbSJYs+2dLNBYbH/5y1+accACTQ5erKK7OhZ7D/ZYTHEtLisrM4RX3BeXY9yebQ5fwDFW3L/+9a8G4H7rW98yaYm4HqsrJFQI80NeffVVZSy82LhMH3vssYboCiIrXJdxdV65UllKFVjPnDlTYLgmjRI5h2G2XrFihemDMQG0cYXGaktMN/HJuFvzXmC5tRZlchFjRQaMw77dGgs07t6AdMi80L0TSJsbd9GX8GL9Qq+rMKMfOGKbFPevUKsRD3PxqAPrcEZWSEYfvFr8gdjDXmTTcv3NsD8QXVQBu3HYpIYquet3xlYeiVdfi7uE1a28xt9T+n//mhZ16Vyw47/vSmTnAhiLLbV1mbpw5TVA1wJeu6e8rj5T1RXzTsAKXDbz3XRWX4u5b6nVMJEKBcJtiHqeqo49snzDmDZapm81C5/vPPhGqwpoUBC36M0FrbZxK0WC6z+Xnxw7JakqvMox8dglv1TAUZa03i1s1sDHM+vlrgdOlDXrC/W70h+3UXb/k8fKvGnx3CrNV7tHaKBxO6C2jR9sGoarJLzpeY5c6WQacAHwbn5DXnjhBbEbsajEmQJwAXWwE1vhR/Gxxx4zGylyAGSQLgEQIWwCgOG6CyAmHhULKaAQIIbgygtYKykpMefc0ykAvh//+MemH0AfMTJYPy1Zl7MtYyHG1aYamjt3rgGPxMQCIgHEr7/++i6PxXkvju+++26jj6uuusqAUsA1LM6kC7LCXBEstbgYT5sWW0G79957m1iSAc1IdXV1E0C2BFu1tbHVdHQIyGaOuFMDlok75p4LFy5sYl82HekLbYYOHWqsvswbwHrdddeZ6kQWaBYZaIOrNm7NAGKEdlj9ec+Z49lnn23eK9I5ITBdO4U+eD/ZbB/O+s54HClbFzes/sO2y4gJ66TXwDLJzquXzJwGyS+qkQHDt8nIA9YpCI5Z58xF/oBEq2O6iuskjU9qahpl+o59pULjVYNqedOPa5wANEIaW7S+Kl8+qhgWV+ee6IOxLkrFi0fd8DMM0CU+Hasw+zoFxpRb8GuvaXm9rUnPfdWKNbJ4ySBZu77YLCTw+XOK+TzqQsKOck3tM2uU1GhqH1eSayCkVt+a7a272NdV1MqGeZ8m78AtNRqonfGcrPnR2RIOhpJqBOb8hXM2yPYfnSCNZS54S6qknYVr5q2UTaUB+d2Dh8u9fzpCnvrHBN2+Jvc+cIQpW70yS2jjSmoNRGo+V6KTmNdh6laxmmjd6raauPV7QAMxNLUHbtzdbolVFmDz29/+tmlqgE1A3YknniiXX3655ChxDTJw4EATe2qZfykDoGJhJBb1D3/4g0nJc/TRRxuA+K9//cvEpwIKiY8FlFEHUMaqyWYFQEw/9IerMAzDjOnZZ581Ma2ANXIBW2Es9El6IAi8LGEU1mI2ygDTkEOdeuqp0t6xpGKBBlDjsswcS0tLjWUUCynjsgLo/81vfmPibHFbxsKNXp555hlhfgDarKwsYwHGbRm3akAw7suA4t///vemLcD5tNNOMxZgGKWxsGPNXb58uQHUuDRjKbYCeIbFm/5ZPJgzZ47cfPPN5v2ijWWBxh2bmF6sv1iOmQ85iXlfbDv6snPk/cBajcs2YtuZE30ZMWKEeb8437hxoy3u1HuPHwtavGTlhKT/UFIisbUiPD374nMPttI6Laq8urDVqA9w728YKL2za6VfXrUUasqjgFrVGzSWequSOa2vKlAypwzpMcjVXeKHwruToT2xHOtko4lFT6yJP/ekuD6+VfqceTNZJBBZpTmAN2wslj6aCqm4Z5UuiIYVEPukUpnft27rIVXV2aad1+eupRtFJHlpfOd6iYYbtCb145YJIfnkTxKtnCyegkFJeknvoroZ/5Cqh3VBv75RU8Sl1kVA05tFdmySbVcdJEW/ekUCQ8enbpzGNf6Ar2n223fkClui+DPc35lEncSde1s+A8XVO09cEiynNjrNcepv5E4zxK4xEAiVEgWgA3jDsgrAtCRKEC8B2LAMOwUSJQidsFJCwASoRl555RWzB0QnCpZh3HyLiopMFVZh3IuxKEPUBDAjJvaRRx4xQBHWZGsd5QLGAsAFNAJysVxiGb3zzjuNKzBxuFiHx44da/pv71iYgzMnMhfj0ox7MS7EAFyAODri/m+//bYcc8wx5h5YrGkD4GVzCmRXgFj6JsbXxvkCiOkTsAnopR7XZwAqrM1Y1gH3T6jLOQLbNO+Bk1EancH67BSs1YBv9GNjjTnHEm3TL7FQAGinT4R2EHX98Ic/bJojrtsAdcS2Myf6gou3FcbTFcTbe6go+5Wu3MRAf4fGrOZNT14sbrtD13XjxoV7995JPOSRrXW5Zks13X77qe5didNA5oD+UrVgYVxZR06ydLHQlWYN+EoX6fcyTNleXXwNyPoNvczW3CL+KNCwTSLBBvFmdOChML6LbnkWnvET8X32lPQqOlJKN/VIOceMQKMMGVQmob8fKYEL/iue/IEp26ZbRXDJLKl84AYz7WzFZH3Vw2hNOYDNE6cKUvBN6L/Tu0p/Y8p/fYkU3/uWePN7xrVzT0RGHbmPzH7ufakrTx53HsjOMG1cXaXWgDd/nDT69JmvsTp1o5013oL92mzjNvjqNeAC4N2scyfog1UYKydxuLj44vIMAESg7LdgC3dcLKYAVYAgAoACAANYly1bJrjb4g4MaMQNGksopFEwJANKL7roInOdfeGeWCphWcaaSYwsoBZX4ERhLIBfBMZirJ6AN4D7Lbfc0uS63ZGxYNG1ZFX2fhZAAh5hfSa+GGCOpXXKlCnGZRhwS11rgr4Q4nfR2+jRo8WZUslem5mZ2cQojVs3lniYmXEzRvfE58IibVMqcY7VnutwW7bg2o7bLmAQC3311VcbizgLGz/72c+My7RltKY9AB7Qyxwh0rJu7ngBdAcWaP/Y4yX430eVeazCqrvde99QJTtTFmNXmjXg0b/BSI+eEtleZfIBN9e0PCoYO7xlYZqXFB99lJT99119Fmn7YSRRVeRhLjrm6MTitD2v+/Rjyd4GAO7RwhU/mVK8CpSLsiql6rV/SI9vXpisSVqWRbcvkcinfzZzP/X4JfLQXw5NqgeAG54Ko4bp7169TwDNgW/8JWnbdCysevK2pmmTK/3GiYvlulcPFg0UEU24Z+oylGsiU7fzxq9uahuprZTaVx+VvPN+1FTmHsQ0sO/JE6TP0L1k7bxVLZ4JPShZhTaupNaAr+hwCXmz2gWA/f3OSd2RW7PHNODdY3fupjcGwNkNt1fiPgGRuNUSj2oFq6dtR+okXJohnrKpdJzWX9riCgwgPeKII0y6n08++cTsiTPFwonLsxXaA2pt7C95ciGYwpprrZa2beKecWKppQ8Ei6oVgHZ7xwLQZ07ODfBHKqIPPvjAxMbSBpdoACHzwAKLQAiGu7BzO++880wdJFc21zIW3euvv95YUJ0plbDQoseSkpImRmlAKJZsXNIB+wBcrM/cH2He9ME5lvkrrriiKa3SIYccYtoAjJkDe2caJBYikAEDBpg9iwi8H1i0mSPnAGbeE46tbk3jLvriG3Kw1IZjFu+OTCGo7pO+I67uyCVp0Xb1vNXy6bId6gYNqVDqKW/VB+QX7p8moYbkcXCpr+zeNYVfnyjWbbejM/VlZ0nhxOTgpKN9dYf2O578g/hD1dJLU0a1zu7ObKMS0HRT+f4K2fEXzfGtjNquxDQQnn2vBqfHFmTGjdoi3znnI1PhNWSBsTbZWUEZ0K9C7v7p6/r7oGXRRomufVtdodfFGqT5a3jD59K4aXWcFvqoBfiRb74vh5dslj65dTKwoEYmj9ggfzx1VvziYbBe6qb9Le5a9ySmAV/ALz/8z62C/1aj/uBEdm4h3dcHvPKb1X9SBy8XHrT2efH48yQw8g6JNj96J23u7XWheAv2TVrnFu5ZDbif8K9A/8nSIAGQrIsve5iEf/rTnxoCJVLzAI6xdFrmZQiyiA+dNGmSAAKJX33jjTeMdTaRBRor5ve//30BJD/33HNmhrhCt8YCTSNAICARSyjETLBRJ2OB7shYEtWLBRvL6EEHHRRXhcWYmFoE8NqjR4+4DQsuAuC1sdTMCTBJvO2NN95oAC+uyFhlL730UnMfu6CApRhAjXV5xowZBuQzju9973umX6zz9MPCAzHVLAI43x90Q9+4j3PsZIF+//33TR92sQALM+NiwcCyQOOeDQCmj+4isxaOUfdIdYPugJRu7SMbN3UcOHfgFl2y6fQnZkhI49s+q8qUipDXAGEypBDvFtY920Zl69zaoIROSqqzeMaSLjnPL2vQPg2BGHzdNeLV746OyuAbrxfvzoWwjl7b3dpHwyGpXzLfTKtfz0rpmVerNrbkKzJeb6PkZAZl1IBNpn1Uf9Mali3qbirZpfngaRVd85Ze26y7A/crlVuueUeOO/wzGTtysxyw73o599RP5KbvzdRFBMdTtD5RR8y1u3TrbnVRw/x3NGNAS6KhvMywfP/g5fJ/p8yRe06cKxfsu1oCvmZdWyVENUQntPITe+ruHRr44y+flveVsHNRXb2sVG9FtsV6/HFVrfz7LzMcLd3DVBoIrghJ2d89yoWliwi6WYkEY+fV70akZuYuhInZjtz9l6oB1w/xS1VvrPNkaZD4gYQkyQpgDJIlQBJpegCxxPcCmCZPnmzAG5ZSrI24QNvYVtLr/PnPfzbM0zbGlD5xmQboWVdoLJGwQBNb7LQW2/tb8ItLMJZjACnAnbhjwPBll122y2Ox92BPzDHWWObnFNyGYU8G2GNhdQrAGPdrxBk/iw5wncbt/Pbbbzf1AE9APHHACK7ICJbz++67z8Tycs4iAwzd1s35tddeo1iWLFliNnPieCFFkrWew+aN9fmpp54yFl1yCLNQQSw2whyxaPft29dYuikjhpqFCxYtEueI5dvODzd3rusKsn5FVN7cOE6+cWzb6TsaFCiTmua/s0fL4SfFHpi7why/qjGumL1spyuaR0rrMsSvP6Y5+lBMag9SH9Uq465196vTB5R1i9bJfie4q8rO96fPqafI5y/PFJn9trM45TF6zT72JOk9+fiUbdKtIryDxTucS2MyqFeZ5GfXy5byAmlQJm2Pfh5x1/Wru2mvAqzE1VoWaxvVBcTw1o16sv/Oq9N4F9bYynBL4Navb5Wcevyy1hWj1vdo5drW26RJbeOWdarH+NzJHZq6enlFylsPqepQf92o8bQXZumzYFQq9K+9wuFBKMGI/PX+l+Xc703uRrP9cqZSv+BDCa+vl+1/UpLUoV7x7+UR+K7C26IS/EwXsmpEAoOaPT+/nFG4ve6qBlwAvKuaS7gOyyFg1RkDjEURiyMWRQCRTYNkmZedXcBezAYrsSWDwn2YawFFxM2efvrpJt0PrrukzIGYCVCWjAUagE3sL/0BaIl5xRUaYJvIAo0FGdDoTINEO8BhIgt0R8binJ89BuRi3bUC2IesypKIwaRtQalt88QTTxigSaonmwbK1qErwC3Wbqy4AF4Lfmlj44lhiAY8Q4YF2Ce3MQsQjz76qOkTizmLCTAycz2LBIBV3MZZNIBkjHEi3JPFBCcL9KxZs5pSGDFH2ieyQGMRps/EObLogFs4AjjuChIJx8jLtu0okFem7SeTDlou2ZrvN0NZYu0DMfMg52pEgcaK1X1lzsKhlEjYdd+Ne4uj+qBcvx7LWUFTeVhBRmUofpHIVkb1oaV2zlQ9PcUWuXvVQF15tfzzmdUyOK9YxheVSUBjU41baYJ2sKw3KrnT/O1Fsv7xpTLsui1SNLhPQqv0PI0qkVWi0grVzZStUf+Ow8qo7VMXXr/PYbG0qtLfnEgX+f6yQ/7S9uE61eMXeLwK6ZOzKxJtUD1+QUlmQf6CXXaLyys3xxbsk02mYlPqumTt07UsGox9PqO6RtOwVD1glrbURHQnZ03LGrdkT2vgC3xD7+mhd677A+BwWSaNTqIAvHC1ta67WAiTCVZRrLmAtp/85CdNTYhphVALF2ZcexFInwCDgEJiiK2sXh3PAp2YBglLYyILNGzIiWmQAIqkPAKcAY4tC3R7x5IqDRJWbhYGEIA8aYxwHbZMyJRZwTKKK7LNjQvovOOOO5ripGlHXDW6sECXMiy7N9xwg3GBtnMFyKJD4nxhjMZ6TPon3KJZWCBeGysv1npnKiv6e/rppw3JmE2DxD0TWaABzHbstGMxI5EFGgswYtuZE31hTlbI+dwlpKFGfGrlCIum6FEQ/MIbB8jAfjtkb2Xh7FFQa1z6atWSuW5jkawrLTbWX+aF5ci3cYkend4lpvllDzLaGJLQ05OlT5FfNmxuBsCt3TdL3f+KfZ9LeOYU8R8+pbWmaVMXrKmXPxzzE0OMs6S8p6ytzpdxRTtkUG6N+BWwAd583qiEIl5ZV50ri8qK1Krul4JARKae8yu5dsZvBObTdBdfYbGm7Ekex4v+fN7kdejNo3GFvp7F6a7C2PwzlXlY43l3SbwBZYEetEuXdreLfL2VV4MVVV1c2SXRZypvD/czmai7WTfdLf2VvXibVmhOhrhqQh76NZTLzEtvka8/dqeqP74+rnGan2QMHy+e7BclWpecTRv1BIbFMqikuao65fRdALyb3xZL0ES3gCLidnFDvueeewxbMa7ICK7OqVigU6VBgp3ZCjGvSGIaJFtvXZ8T0yDZeueeLzhnGiQYqCHkYvzEJeOCbQUrJtLWWFKlQcLVmbhYrKRO5mYbG2tdnLFGk+f4gAMOMBZTxgM51q233mrAJ2MA7HNOvDQs2wBmxo47M2RWAHfL4Az4BRTDBD179mx58MEHzRc7Ls8AYKzOvCdYyS+55BK6N4KVd/r06cb6ixUfgXTMyQINiAfw27FzT8aP/hJZoLnetuO4q0rVi1Olt5KPrN+er1PgB9Ij6zVfKFuros8xuZ8+L5G6a8Wb3fF4zVb77oKVjcv+KdGyz+TgfQtk6apiqatvG4TxPDJu2HppnP+w+Pa7XPOGDuyCM9+9Q57957ekemuF+FQ3fBqrwwH5cEtfgVUgoLGqWbrwUq/Wy1Ck2aqOoy8Pe5Uby+Sjv70jh13W/D23e0fXdXrz5eVLoKiHBDd03AIZra+V7HEHdJ3Jfokj9WiKOE/fCRJdP7PjdwnkiGfQ4R2/rhtekTFKyRZz8iVas4vcGaGgApB9u6Fmdn1KH996v6x9YZocne+RBeU52hGLC3xrIhrioMcnFQdl08y5Mv/2B+Rrv/hBrMp9baGBnMOOk20P36ULr6ml4LTvpK50a/aoBlp73/bowLrqzbHG2g3w1lEWaOJjEYAcYlMPAdKoIwYYF1ysv8TlYk21oNRcoC8AWsAcrs/WFRrrcnvSIAEccVHGSglYP+6442y3HRoLLtTEDjs3gCEbFl1YrZ3MzVhwYUi2KYIA5Fhp16xZI7Aw4w4OMzOuxpQh1hp9zTXXGJ0TOwy4pD2WXOZObDVCSqYzzzzTWOGPOuooA3Kpt9ZlUiqR/5g8zOQatptND0XfNg0Sc3CyQF944YXmXpZwCxdrNlIuWRboa6+91oyF983O0QysC75AlFP98p9leJ/NxrrW/ilEpY8yy2Z56qR2+ovtv6wbt2z88DfKFFslB44rlaIe9QaQtTXdSRPWSmGBuqqqm2V43oNtNe/29fwdv/vAKxKsaTDGImKnnQLorQplxIFf6mnHYgLW45n3v2T+hp3Xpetx7l5lmqYsXodt6kJjg7OL6/Tz2zLutc1ru2kD3/7f18BAFgg7KHm6GNvHjaNGa4HRB2pMZduLgkk1rLHsgXETxZMRewZI2ibNCqtWrZe1L04zsy4MaJhcSY0MyIxIhv79sg3Oisj1g3Qhi3VCXcBf8/x/pHp1cyaQNFNXm9Nd8cFKeXlObAEawkqn4HX0warB8sGLnzmL3eNOpAEXAH8Fb0YyFuhUt7XusUOGDDFNALcA2v3331/KysrkrLPOMuAN4ItLNVbJxDRIgF0nC3R70yBZIiwAJsRO7EkJZKUjYwH4OVMgccx4IeoizpVx0zc5fHmAxZINuGeuWIPJfQyQJaYXCy/CmBBAMMI51tfhw4ebc/vCQgH6QZdYmhF0wn2sWLBqy5599lljrbcgF10gzpRKNj6XMTrFvme2zNbbPeXcx27OcntNV9o3LJ1n1owLc+plUHGZWt3a+7DskfGDNgqWotqZL3elKX8pYzVEN7Uxkja/Xx9GrnjXrL4706Q4b0x89YiS7XLhqTuZdiNhiXz2b2eTtDzevGSdYca2kw+oq26OWnxj0vw3b8+x+uZqPe2s1FfWyrbPN9rTtN1HqzZKbs8t4stEf836aVMhGrPeY3hQ2Yt3weLZZudds4Fn6Mni6TlSop5mr4P2zMR/xN3taZYWbQC/OWdco26mHV9I8KiHUd65P0oLPbV3kqv/+YaEa5vjqrMUAVwxoF5uGVJrtu/2rxeAsZVwbb2s0mtcaakBnufeuvVJqazPkn8tGCsrtxVJlXpw1WimhvVlBfL28mGyQsvmTn1DqtyY6pYK7AQlrgv0V/AmJGOBxj0WcieEPyRAH8COPemHEtMgEXeayAL90ksvGWIt4oZx1W2NBdq6QkOAlQyAWfBrWaABn1hcceElPnb8+PFNKZk6OhYzyZ0vxBZj2QXMQwbFfXEfRshVjNh0QugIsYsBMDdjobZsy/TD5hSueeedd4z+sLZaCzBx18T2EmtMvLMlK7OM01jrcdtmg5CKmG7qAL2Mi35Wq8s1AignDRKLEVjoSWGFxd0Kc2KcsE4zZnIPP/744waQA5bp096Xa3DDtnOyINv21Rn3jZvXqdlMLZAq++29UeqDAdlYQfxq/MKAaUCpAuQMBRxHjflcsjNiMYThTWttddruoxVrVDnNn5u83JD8acrL8uQL+8n8JXvpoo2ySOoqsncnUDth0gr5xtEJq8k1LmgrW7dVwy3jF2EyNSWKzxNWt2evhPV5jkc6Pp1+tXJkqV79zWpv+vzRT+8R/ZvO0/EgunWpWtx80me/7VL6wV6qAqu5VrShOu17wFaN7w9LdKvG94/+ZiuN06fKoyRY/tP/KaGHSvS7n0XY1uceVMLA7BN+L97Bx7TeMM1qc0+6VIILZkjd/Bnia2dcdVR1X3Dp7RIYPCbNtNX6dLe8p4vXCd+VrV0R1Q/ulvfnidzUWqv0rKtcv03qtsdc82uCmfLh6sEpFbHm3UUy7uwjUta7FXtGAy4A3k16t1ZAC6zoFiskrr2JLNCANEAvrMWJAkC8++67jcXSpkGCGAlG4WQs0LA8I5BhAYAZh7U0JrJA2zRIdqxcZ8eSjAUaIi4syVhZIfECnHdkLPSfTJjflClTmlIEAVRJYwRQRKzV1u6HDh0qEHVhkQZQWrdl2zd6Li0tNUAaIMn8rrvuOlMN4zZu0RBTAYxffPFFA1atqzlkVYi9F/exgBxSM4T3kD5tG1ypbRok+oaFmgUIOy7aYfXHynzVVVeZPnClxh0eYrFEFmhimxkbwuKEjRM3BZ3wpbFihxLlNKemOGT4Wlm9tacs3dhHyYaU9VmtQQgpfEiZ0q+wUsZqrlALfqmL1FSxS2uJ1ikFieb8dEqWWt6uPO9jqa33y+oNhUoepiEJ+fVSMqAiPleovciXoRZ1dVnNioVO2OJ02tdsqzTfY4lzBuTmKQFWeySiD4X0k+4SrY19Jn3qFjng6xtl64JiCWn+6aiCs0TBTdqrbuTF+5RJRl5sYSta6bpLOvUUUX3MWFgivXuUyaDeNZKfE9OTs01D0Cv1yvj+lzeHy9V9HipeOZYAAEAASURBVBfv+Ev0d6Clvp3XpNtx4c1TZfMFh0hueItkJIQ4JOqiVtN11e13tux1+FmJVWl/Hqzo+O9usLzj16SDomt3VGmoSNt/p2S9oK0rnU8DLgDeTe8JLrZIe1igndZC5+3pgzhUwC7g06ZBAjSlYoEGXDvTINEHYA1CKOJUE1mgcYcGLFphLIDgVCzQ9AX4PfXUU83W3rGkYoHGMkoMLXPECgyAB1gTM2zFsi0feuihcv3115tiADogE+DKmKwkY4EmjtemUlq3bp0Bz7Y9e+YLCEVsaiNyJiOAaSu0Q3CPxiIOezTC/YlXBjwDmLEIs+hgx2VZoGlv9Yv1GMu+vd4c7HyB4IvFCYTY6M4uvp69lPE1U1eSm5kPS3qXyWDNGVpRlyU19ZmGbTfLH5aivJqkDyzevOZUWJ19vl/W+Dw5SqqW4kE3JyssY4fB0dmGNGraqTQGv2gnv0+heNvxINKaJrk+v4/7mfTkqdV3p8uuV93y+0zYJvU7MqV2c7Y0VGZIJORV0BsVf05IcvrUmU35nnaK5sAsTG0Fsa3SZR/Z8omE/zJJinJ6yo8fOUhGDaqQkw9eJ/sOK5McZXIPN3o0v3K2TJ/fT/79wd5S0kfz/26fJ+FXr5DAKS0Xx9NFb8nm6dHnlPl7nypl/35Ljh+13qTiygo06iJrrDW6DCrJXYVmHnj18xL55lWTk3WT9mWZxYVSvab5+a89CuEaV1pqIK9vTyEdZFviz8qQPP2NcqXzacAFwLv5PXFZoHeYHLipWKCtdRMrqQXBdvHAvhUAZARgjDz//PMG/HJMHmLLogyohAWaOGJclMlnTKohWKDpG9AM4McSDtAl9y/3HTRokGkP6B0wQNMsqAA8SYPkFAAwbswsPgBw7bg6wgKNezSgHRdoFiUQO357r759+9pDQ27WdNJJDwIDFcj7Ay1GR2g0ccFsbUlg8Mi2mnT7ek+R6kDjeL+IeHqUfJHLu8W1vYb3+8LzILdy7xGx74Iv3FkX7sDTR1N2RJq9O/ibzi5uMFub08osUObj8W02S4cGUfXsCL92pZnq6EHl6h4eldlL+5gt2fwDGiIy+cB1urraIJGVr0vjyjfEN9QFcVZX4bLpMm7Mq3LPgyXy388HyvDe5TK8V4UU5dZLSMMcttdkyeJNmte7PF/yC8MyMO8OzUm9j3gzv/h3gx1Dd9jvdcRBsuOT5RINte93x5sRkH5HHtwdpr7b55C/V08p3LuPbF60utW+o/ocWXKEy0TeqpL2UKULgHez4p05eW3XACfAGNbKCy64wBTj6mrJnWw7LL7z5s0zllFYoIcoEZaTBRrANmLECAOgcI/GeoobNeRUF110ke3GuNFieYQFGiAIALz44os7xAKN2y9uvKlYoNsaCxZdmJedYgEkccjk6iVHL8RYiXGvloiKMeD2/MADD8g+++xjUgthoQXAIpYFGtdj+mLxgXa4QRN3TR5eXKZhdHYKIBrmZt4DdI4AUjknFtgKOYitVZd+7LiYB9Z1BJd1+uLelliLGGA2ywJNO1igeZ9wq+7qLNCBoWM1TjDQEYocVNAkHk1rkTPppKbzdD3w5PQWT48hGje5cNdU4MsU78jTdu3abnRVUUlfyemZJ/UVzR4JHZ1eft9CKehf1NHLul17T06xeHqPleiGOR2fm3ozePee1PHruuEVkRWvSbRyvZkZ4Pemsz6RW6YelHSmXuVIyFWPj8kH7HQfb6iUxpm/dAHwTm1FNe43tPo3MnRsnYyeUCPz3tWUcZuLzJZMoWd9b5MEfFUSWv8nyRx2W7ImaVtWcvZkWfLgM+0GwOpaIyVnn5C2+mpr4pPvukyePPXnKZv5MgNyxI/OlZyijpO4pezUrdhtGmjbgX233Sp9O3JZoI82QBOwi2ChhbwKFmiAZaJAEAUQJf8uaZSwzB522GGmGcD+oINiDxKwRhcVFRmXZkipAL8IYBagDHjFcosbOJZcK8TcArqJPbbEYcT04m5NOiQEEiwWFwCrANuSkpImoA5QdooFybbM1ts95TYum72z3F7TlfYenxKMnHOVut7G3s+Ojp38vzlHnNrRy7ple9/X9cdzV1KloA1/tvi+pqlW0lz4ezr6R2dJZl72LmkiMz9bjv3fs7v83+UuTT7JRf6jp4hkdtAdXBdjfIderSln8pL0mH5Fhp29obxp4hPHbpHfXvmhOc/JhFtC3cjV6pufHZQJw7fLsz97W8NlmppLtHyVRKs3NRek8VGkar6G28S8wa6+c630HRgUX5I44Jy8Rjlbwe/Rp5epevX3f8dbSrHQkMaaazn1nH69pczXQ3k6WtYlK9kRKJLsvjGPvGT16V426NAxctqjPzRqcOpUH/OkUbd+E8fLIVe5zzqd9XPiWoC/gnfGZYGOVzJAkxja1uT88883VnPchSHMgukagXkZyzjy/vvvG2vvySef3BQHjbsy+sb1GRAMyMWKjSUcJmvA6htvvGHKr776aoEkC4ElG2B+7733mvzKWJ0B4sQKk2f4y2SBNgPoYi95J14g1f/5hwRXLWmKw2rvFHpefafGEGe0t3m3bucbeqJE+h8qjWvfEU8H3aF9k36u6UGKu7V+2ju5r539dfng4deldMGq9l7S1K7PqIEy7rRDm87T/cBbcoR4J1wmwZn36fdk2yRijRp/Kbl9JGOSm3LGfnYipS0t6AeM2C4vTnlT5izrLeu25hqr7z4lZTJ273JdfLFX7tzrImNkywLxEZOd5tJYOUvRRLXRgqpFfv3McnnvtUKZ/mKRbFqbodE4URkypk5OOHe7sRA3qcsbkEj1J+IrOLCpKN0PKjfukI2VAWkIZUqvzIaUv91hzT6wPZghZepezjUF/VzvmFSfnXdfmCcrazKlQDlPspUYkL9lsg9UKrHdjnmb5SJ1gcZw40rn04ALgHfzewIbsxVIkrAkAsjIe3vSSc1un1gCd4UFGkCHAPp+//vfGzdozi0LNMcIVpFULNCW3CnWMmadTMYCTXzt5Zdfbqywl112WQsW6PaOxd7H7tsCv7SDQIo54DKM27hNG3TppZfabuT11183x8QbJwqplHCLph9coNeuXWvclHGP5ssIgA2YtoJFGrfs3/3ud3LDDTeYYu6JXk47LeZmmowFmrmQj7gtFuixY8emZIHGQv3222/boYgzJripsJMdeDIyJee6+yV8w3FqgVfG53Z8vxOrtXXQZBl04NGdbDZ7djj+0/4q4T+WqB4bldyl7aX5BmU5zTnqFvF/LRZjuGdH3znu7gv45dJ/3CJ3jLhCB4QOExFFsnHG2l350q1qUWpickrWMO3KQvvfJAsfeknGjV6pDPqNKeff0OCTLdvUbfqwqTKsBYpLeVn3r6jbnnSOPTTV2XETSpPWxRU2qpW4ZnNcUbqeRBs26tTjvxcnnVQubK2Kuk5HQztabZJulYBZr37XlVbmSnVY43uz6jQfevwiV0gzOWysz1YApzmYs31SuanMBcCtfFDWzF8tQV0w2KbpIBPFU14jwdqgZOXFCFQT693zPasBFwDvJv1jlcRaSK5ZK5A7kdrnxBNPNEDSugBjdSQv7TnnnGObGjdeQBtxrVgrk7FAn3DCCbJ06VJjxSR29f777zeWTycLdElJicmjC8glBjeRBZrYXGJwrTAW+kxkgT7wwAOFjdhWgChpfGCCtizQbY2lNRZoe29icRlvIgkWzMp33HGHia0F8M6dO9fET5PCyDIx04dzsYF4YdylYdH+2c9+1kRohR6wBJPTGCBMf2effbYhvQLQcm/bJ27WTz/9tGFk5p7M1QnWLTs17YjpBfQCnIkJxoJsXaFpxz15H2Hc5n0ll/LLL79spm7bWT3wOYDxGuE97SqS3a+/PLroEDly70XSM6cuKdszc4GhM6w/qh+uGiSDjz6yq0zvKxunJ5AtNWcskFV3HS0jBm2SLGWI1T+5FhIKsyDkl+kLJsgZP3atbYkKyi0ukPGDPfLZurA+kPhE1+ITmzSde/WBOsPbKCOGZItf47RciddAhrqTfzh7lKxZmS+TJn6moSC1+htFTmpS+HnMFlILx6xZQ2Xl5tHyzW/nxneQ7mcBDQ+p/wLgCybuXQ2N6Ga69/g76I7fNH9lJfe5LvlN6tCDzPwcE45FGQCXza8x6Bk7QXBQf6fD0ebVbAw1WRoi4kpqDeT2TP3dF1G/6ECW+/uSWnt7tsYFwLtJ/1h6EwWgAzPxp59+agCmJVEC/ALyiIF1CrGmpPDBoomF0rr6Qp6EYI1NFMiwsGoSC4usVmZk0gvZNEgAM2JpH3nkERPvCqkWllErjAWAizUZkEu8MiDyzjvvNPloFy9ebKygWDCR9o6lNRZo0gb95je/MUAcHXF/LKDHHHOMuQfA0Y4R0Gpz9VIJqzJxuwBPhL4YKwzPCODTCaiZL9Zrp5DWiA0hvRFuzwgLAwBvmyKJHL6kgMLCi1gSLxYpuGbz5tgKPQsFMFZbdmfaQWIGCRd9MkdcqG28sW1nOtUXS8TFeTKvANuus+0zcrMks1eRvL54pPTvUSWj+m6VvQqqzDDJBYw1s1ZXRVdv7ylLNvVWN8lCOWz/4Z1tGp1iPEUD+8hd046VHp6Vctykz2W0pkDy6UNJWK3mAXVDrarJkIXL9pL/vDdS9jn1uE4x5s44iF7jh0vD9rlq3fDrinymWXiJtx55jMWjOKNB8tRlre/++3bGaezxMbEoUDi4r6xb2iBPP1OsPAj10qd3leTkNijBX0B/Y3L1d4cHP4/itIj0HT9kj4+5Mw3Ak9dfolXrd31IXr948l1WchTozRnBi7KTNz+3tE+xyreRPbh9TdOkVfGwli71AF5+Z1JJ8bB4MtNU7dK1fOK3JsmaBWukoSY+3pzn2pGHjXS9izrxB8PficfWJYf2pz/9qWncxKNiAXz44Yfl5z//uQE3Ng0Qbrh//OMfTVvcfLGYAlStK2wiC/QZZ5xhUgABqHA9LlHLKSRQxKomskDTKffEUgljckdYoLF43nzzzQa8AdxvueWWJtdty0jdnrG0xgINeMRi++STTxpgPmfOHJkyZYpxE4cZm8UErMNPPfVUky7tAemESGl0++23myLclj/55BNDpkUfxAU7+7JpkGiMvrHIw+AMYH7zzTfliCOOMP3wPpGL1y5S3H333cK4yNGLjkeOHNlUx3XED2MRx3KOxRmLr2WnBgADzAG9zNGZBgkvgK7OAm0UtvPloMtOlLd+8WcprSgwm9qG1BLcqCycjQb8RhyryTnqejXkKBdwOPVnj8lDe8K1J8s/f/G0PPZcTy2OSk6W5lrNDklFteZVVsuvlROubg6lsGXuPqaBMRefKls+Wiz5NXWSH9Bcq2q1DPGAp3u/LsgE1NrBHvFmZgjtXUmugYOvPl2m3fKo5v+t1fRxWbJKt0TxaCLWUadNlMyC1FaQxGvS4dw7+ixphN09XL/L0/X0O2iXr+1OF3p7KAGmMox3VDyBXvo37i4iOPXGc+fXzjtCZj32hjSGUoc2cI3X75UJFxxlDBTOPtzjeA0ce8WxsnTmUvnw2Q+aKjJzM9TymyE/eunmpjL3oPNpoOPfKp1vDp1qRAA4u+H2+o1vfMOASNLikAbJCqtDth2pk7ACArZsKh2n9Ze25KkFkALYsIoC+tgTW0xeWmdcL+35oiMNEuWAwB/84AeCNddaLe04EveMk/Q/9IHgvmsFoN3esQD0mZNzA/y999578sEHHwjWVdoEAmppUKZl5mHz5BKHi2UWHZADGNdhm+YJt26AJ/LOO+8Y92gsqli4YYfGNdnZl02DBCAHVB+jVmZ0iIszcdQ2vpd7s2BBminGhEsyZF0AZqzVCDHBzIE9IBqASxokFiIQm1OYRQT0zr2YI+cAZt4Tjq1uzUVd/KVo7+KdRBoxUKFr7tKg1rfqhkxlmoz/eikszhJiNV1JroF9Dhws2U2spR6prc+QbWppc4Lf4X390mdwr+QduKUy8NiDpS7iVze/mDIAu9mw7SoYZm/Br3qmSYO6qfY/fIKrtRQa2Oeco9qIXVMlqqKPue27KXpI32LfmHNElBl7l8Sv35P7fbdb/U7skh52XuTN6CO+ohMUBHdAnx6/BAb94Ivctttee/j3jpVMYWFm55dk0plGNUV9RI78/nFJa93CeA18/8+nyPefGiITLyqWA84olHN/vZfc+9np7t9wvJo63Vn8E2qnG173GFCyNEgApGeeeaZpw7X3pz/9qa601xhrJuAYyzDWRoD0unXrZOPGjTJp0iQ59thjTUoe2Iwh1sLNmnhUK7gAf//73zcg+bnnnjPFuELjqpsYf2qvYQ/4hfRp0aJFJp522LBhxoKJtXRXx+Lsn2Ms2ABHm8rI1gNQnXOYNWuWidUFgPbo0aOJBOtvf/tbE+AEqCOAVeKg0SeuzZBIcc58rOAajgCqSYNEbAug1ApWZ+6Fazqg1bpRA5aXLVtmwDNuzrhm0y9pl8g/zJixOiN2sYByQC4LBuiSRQdANACYPrqTfPbCu5LjJa0HkuwHlTKNI/JqXOa2Mqkq3WZaui8tNbDgidekRNOi9Alomg/VmXPLUMvl4Kx6KfCFZdW0uS0vdkuMBrav3iIra3XRQF3wAbnJhPQUNY0+WVmZJeWlXyBOM1nn3aiseuN28dRVqcs4lqLY37Fz7/NEpUdRpqyfOb8bzXr3TIUc374JCsB2JS2Uxv76Dmj+bdo9I+ravWQM/pHi3376u932I6v6IIm/74XiLzq+a0/6Sxp9aPECOWH0Fu1dw5T0dyVR/B5dMMwIymnjNkjDonmJ1e55ggai0TIJy/Ny4Fk95dKHB8tVfxsqR17WS7MULtFUSJ8mtHZPO5MGXHPMV/BuJEuDBAAj1tUKoJD4VUDSTTfdZAAY8b0ApsmTJxsrKJZSrI24QBMDi+X317/+tUkRlMgCjZvyjBkzmlyhAXW4+BJb7LQW2/tb8Dt//nxjOQaQAtwTWaB3ZSz2HuyJOcYl2AJMW4fbMOCf/Ly4DyNDhw411mj0YMElANTmDsYVGiF9UTLBrRzrN7JmzRqzR58sHOAWjjXWCkRYxBoDlMlBbIVFBwRQawE1iw7kDcZFmz6IEWahglhshDlihQaIY+lGiKFm4YJ7M0fLak0dnwNc3hHit7sCC7QZrL5sX7pGwb5IvsZTQqABg6TzJ9WvD8mZannzaRuPuvmWr9gg+f1dC6bVn3O/ZWHs89xHSbB6ZYSlQd12GxXIBdSKmaF6RM9BdUfd8skqGX3GEc5L3eOdGti8XD1W1F1ySWWBEOfbWx/kYg95HtVf1MQEb9H0Hjs0Pji70Cebl62Xwv4x/gRXifEa2LF0tWGMzfCp67iCYD6LWNb5HHpVl+r9LKHKGtm6aIUMPUndVF2J04B/0k8kuukjiayZrvGrwbi6lCf62c24+D3x5LjfkU4deXw5krXvP6V8hqbokloJZLMg01KCNfob5J8ovQff2LLSLTEaqPn0E/HW18g3h30ma/R7kq1WGaGR3EBI9s6vlMEFleJriEq1LuAXTT7Z1LkvyTUQkVKtyNAt8W9creiyRhey90l+oVu6xzXgAuDd9BYMUcsi4MoZA0xcKLGuWCMBRDYNkmVedt4a8MX2ne98p4kMCvdhrsV1l7jZ008/3cTHYsW1aZCwLtLGSklJjAUagJ2YBgkwCLDFCmuFsWBBTpYGqX///i1YoDsyFnsP5x6Qi0UXoS9SHKE7S64FARU6Q7CyAnKx8AJ6sQrj4vzoo4+aRYDRo0eb2FtiewG4uC4D9F966SW555574oA+rs5sN954o+nLuiubG+kLFndc1NEZVl/czQHFuGsjjNsCYFybWUxwskAzNkCybQspWSILNHMEADNHdGuF96rLij4EIzwUZ2oOPDbndCiPkxYFcbXpfeLQDeAiW4FHMqu6o1l66yvJ7PG8iH3kyGOZZTZYTrFWhhXAAeL009p8pavMZl0kHulirBXUxGJWMulOIR3J5vdFygJn/VNCb/xAIstfVJ/7itRd+ZVpN7NQAmc+K548l3QomaI86ta84K2LpXbVs7Lf6ZWSUxyWiGYY8OoCISHC21cpUeDL/WT///m29E7WgVsW08DO7zxIKocWVpjNVc0X0YDj9+SLdONe+5VrwAXAu0nluNDiJoyLbqIQowqbsE2DhIUwmWAV/fOf/2wAIGmQrMA6THwqLsw2Dy5xsliCsfwSQ2wFC6aTBToxDVIyFmjcfhPTIBHbShogLMKAY8sC3d6xpEqDhJUboJ/IkIx1FAHcWwZoYpaZN9ZVwChWb1I2ERPMYgB9VVRUmHRSlrkZKzkLB7Yvc7DzBWsuxFaAz2uuucbEENtcxqRGol/Ir9isAGQBvlhsreUcoJzIAk2ddS9vzxxt/+wZi5UjjzzSHnaJffHIvaXss/jPcypMEW2MSOHQZuDfJSb4FQ6y9z4lUr6S1eTUkqEpKXqPG5K6QZrX9Bmp7LsJOM2wnCaUoSY+j31HuiQ5qT4yRfq3jY5ak4z8HOm1z9DWmqR9XWDyHyUy+hxpeOfnEtzwiWHctemkIuoxE8jOlNwD/0fdnq9S1uLitNdXawrof9A+8vY/95LP3smXrB6aXUG3xpDyJZT5lG/MazwWjnc/j62pUHLH7CPbs3MkUtc6q7ZX+U5yx7rWy1aVqZVeYcEqOaGYV0rautyt34MacAHwblY+eXOtAIoAXTAyY5HEYomFEsHVORULdKo0SLAzW8GSiSSmQbL1lgU6MQ2SrXfuWcF3pkHCHRdCLsZPXDJuz1aspbatsaRKg4SrM9ZV+k5kSOYeLBbQBgH83nDDDSaP8uzZs+XBBx9US6NHlixZYgAw4BSAzgIAgJQFBsiwbH7gxHRDNg8vjM+JAsDF9RhrOERbWMYB06+//rppiquzJSjD9dzJAv2LX/zCsHjb+7Vnjon376rnY849RtZOe0+C9W2vghYO6um6P7fyRu/3ncmy8uV31UrZbHlLbB6sqpOSY5pd9BPr0/28V0lf6TW0r6yfv6pVVQCS+44a4Lo/t6KlvH7FMvDr+8mqN2elBMLBqlrZ+6gJrfTiVqEB7+CjJPuSmVK28FN57Lwbpe9eEalWd92hhx8mp986xVVSOzUw9MRDZfZ9z6oxXT2yKnxmc1466swjhc+tK6k1UHDYRPGot5zUpW5DTVQ9GHtoW1da14DH01P80dM1Dvif2pDnIFZbffrvAPU8Gtv6xW7tHtVA6ietPTqsrntzwJjdiCXtKAs08bGIjQm1qYeweFJHDDAuuFguyW+Lm7UFpVZrgEQANizQ1hUa6zL5cpO529KWHMAIbsS4KGPNBKwfd9xxtlvpyFhwtb7tttviNoAh1lS2K6+80vQPsMStmRho5gTo5BgBiJ555pkG2B511FFyySWXmPFbV2NczhknVu7hw4cbyy5s18wRKyx9OQUAzFz32aflquby5csNMzTpqrDiQ1pFCijcsxkX4NimSGIeThboCy+80NzTAuT2zNE5rq58PGjvLdKrWF37UrhHOuc26YhlzlP3OEEDVa88L31zaxJK40/HDayWyg9jbvnxNe6Z1YBPwyUSrcC2zu7xUsjQHNautK6BI+9WDgP9jo091Dnb8pAXlW8+9XMle8l0VrjHrWig/777yAF3/kQ+zj9Itu53rJx8109bae1WJWqA/NSHHkI6LiUJ1NAGpbpTC5ymNlPipr651XLgySMTL3HPEzQQ6Fkkg25UUjF9rkklHp9fht1zn/gLY8+jqdq55TEN+DyDxV92pD64b5Tooo3i3TxcP5Nfd9XTyTXgAuCv4A1KxgKd6rbWjRbghQBuAbT777+/cW0+66yzDBAE+GLxJLY4MQ0SYNfJAt3eNEgAN9ydiaeF2In91KlTm4bakbEAnp0pkDhmvMwFsXuOAax2o5x4W4R5OAG7BZi27OKLLzbtrPszJ9a9HJ0770EbSKpIc0R+5EQhvzDtneAYl3ZcrE888URTB3kV4uyXc/uecYzYerunzM6PvbOcuq4sjR89IieeMk+yNWet17DFxs/Gow8pGZkhOe2Mj6RX1jKJbFse38A9MxoIlVfI9hkzpW+OspDnlxvSIZ8+1LF5VYc5/qCM6rlVAsEqWTv1KVdrKTRQta1Kls9aIdWh2E9bIhDmHHboKq3/dPpiqSlrfcEhxW3Spnjx75+QXpqLOkfZx30KNMjzDeCA1b04o14W/frhtNHF7pjo7++cKlec9zN56bX/yt/+8rIMyT1aKsqrdkfXadFH9fx5UjX9P3L4oLUytvdWjV8tl6E9y2S/vptlVPF2WX/v3dKoIVautK6B4pNOkZyLr5dG8qRrHLUVjoONXsk+7wrpcWjL5yTbzt3HayCyY7U0/PY4aXzmIYn87SEJ/d+3JDS7+dk5vrV71lk0kHoJqLOMsBuMIxkLNC7ATzzxhJkdoAhrJkRK7Ek/lJgGibhTLJpOFmjIniDWIm4YQqaJE5vdVRJZoK0rNARYyQCYBb+WBZo8vFdccYVxUyYN0/jx45tSMnV0LM63kPtgZb7vvvsMGRWphwCfgEg2QKa1AANmf/vb38q3vvUtE6NsCcYsgzJxysQmEw+NqzRM0RbIQlLlFOtuft5550kyF2isvGy/+tWvzPgg32IsgHCbLskyUcNSDUEXixFYxUlhhWXZSnvmaOfANeQztsDdxj/bvjr7PrJpgeooKhdc/K4s/nSALPl0oFRXZak+YrGDAwbtkAkHrpaiIgUa0TyJlM4Vby93lT7xfa1aslRZsn2muDi7Toqy6qRBU/XgDp2hCwsBJRezUrtqjbqkNja1t+XuXtne1fXZF4hBtcoguoua3L+suwF+w/rAF9RNl6HE5/fJ2gWrZcxRLT1CXF3GNFA6TdPrRRolV58UcpXpPVHqNm2T2tItktO/T2KVe+7QQKRio5T+6zdy75Q5TaVhja8mL/WvvnmS3PmgWtLHNIcaNTVyD+I0UPHuDF1h9hrLb09NC8cWJyygf7JACg4+NK7YPWmpgUWzS2XpmhJddK2VHpn1qlPNQtGQJVs0jdzoRZUS8wlseZ1b0lIDDY+fbr4nnTWhl/5X/PufK56MXGexe9yJNOAC4N30ZlgroAVpdJuKBRrwC+h97LHHWtwdUAcJE8DLpkGCGAlG4WQs0LBGIzYNEuOw1sZEFmibBsmOlevsWJKxQEPEhSWZGFdIvADnHRkL/ScK1l2ss1hzbYog3K9xFYd0C0st1mNco3Fjnj59urz44osGtFsXZGuJpW/cuqdMmSLOmGSYpSHMsgKoh7wL4b4A1EQB1BJTjOs5+gHQ8h5gBWYMWIGtZRq3bJsGibhfWKNZgLCu2e2Zo5MFmtzFb7/9thlSssWJxLF2qvNQjEgD/D9u/AazNeoqcijkU+KwsL5vjtE26gN0g2vtcGik6bCxtk4zpdh8yngRiGT5cT1lixc+I4119eLPc39Y4zWjucyr6qVxpx4BuaSSamheO4hrHtG/8/rqlt8FcY3S/ITPWWtCarNQdew7oLV26VzXuPJ9qX/yYikrrdf8qoOkKhhb6EInLMhs3lwhDU9dKqGhEyX7sufSWVVtzj1cqeE20RR/0FytizWRnR5kbXaWxg3q//OolH3wti4G5su66gKzOdVR9t5bUv92L8k65hJnsXucSgPhYJIaXXEN6fenC4CT6KZzFDWbrTrHeLrsKABLCPGjdgPYYN2DwdjJAu20FjonTB8LFy5syg9s0yDRxskCjWV20KBBxmJJCiQ2YlYR+gDA/eMf/zAxtMTHLtJcbowFV2g2J8hiLIBgywJNzC/XMm6spvTF2E899dSmdEvtGQss0FhRnRtgElCLoBOrB5irqUO4H9ZRYnkBw7ggI4B6XJiJv3VaTzlncYDUSIBeACn6x0KLwHB9xx13mNy+xPGyCED+XsRpbcXdm5RS3B9BJ3ZMLEhAvGXHThsAOkL/gGfGZ6+17Vqbo7l45wuLJsuWLTNbV8oBbIaf3TKOEotwVlYC+KVxIFs8+XvtnLW7c2qgvkbzKNe0DjZs+2B1nQTrkv3g2hbpu8/whKSxpn0ukOSw9VSWp6+y2jHzQHHrMYDh+qDkDuzbjp7Ss0lk2yqp/9PJmkdvmwzMq5Ge2S2t6KeM0t+4YI1EPpsh9Y+fm56Kauess0eMEo8+O6QSPGMydYHdldQaaHj3Wal99g7p32O7+JKELRFyM6Bwh9Q++WOp+/d9qTtya5o04Bv3TWW6a17YMhVhDZnLbv37s6kD92CPaMC1AO9mtbss0DuMJbU9LNC4DkPshdsyoBuxTMolJSXmHBdpSLmGDBliXJwBiraNaaAvWFwB+NSR33fkyJHGagxJGOCYfMNz586VU045xbiQ4zpOOqTrr7/eAGKAMwsKEGHh7mxjjQHBsGMDhLmnBbYdYYFubY52/F15H9rrKPGU/VX8Jmdt6zMJaWxW1t6TWm+UhrVV67fIKz+4T4b1wFyuq8atCG681eFMef3SO+R0dan0qhuvKzEN1G3dIUtu+bUu8KllvHU1mgu8Sty26vcPy+jJB0hWcTxhnqtTkcotFfLRggoZme8xrrqJOuGzuKFM5OOXP5aDz3X/rlvop7ZM6h9odmvO0O/Iv5+7SiY9MlpyAxryozmpL5uwTU4eWRm7tDEojSs/kOD0+yXjqObUeIn9pvN5z6OPldKHH1BXj5aLhfzJezW9T9bgIemsolbnHpzzstQ8eq1p87UhpTLrsxI9RnP89vAaVY4Erxw4bL05r3/tAfENGCUZB5xkzt2X5BrwHfBtCc95wixkmRa+TMk45yH11ndtjMk11jlK3XdnN78PlgGavcsCvWss0LwlWGlhdn766acNERdu2OTLxcpqASpWWUi6LrroInnvvfcMiOVa9A7oJWaX/MxYerHQwgJNP4BfBKs36aIQ4q6xLBPXC1Bmw+UbEIwAgK0LtssCbVRiXl592q8LBG2vo/GwvFDjgzcs1SdmV+I08PH9zyqQDZjYq7iKJCe4Rm+tzdF8wRtk3fS5SVqkb9GyR/+hZOQRGaGWtvYg4BF51RLW74blT7yQvkpLMfNwMCy/nXy7bKzPlk31mS1a4brboDlsF1cUyNM3ae76VVtatEn3gtCHUyXaUB2nht65Yfn0mkUKhFfKaxd/JtdNTNCbhoiEpt8n0VD7vBjiOk+Dkxpfllz1XmyiYYcndJ06blVoPuBby0bowkIaKGIXphjVZ5naZ29vujIrIyxXnzRT+vWslAyN72cb1KtMvnfC+7qgHVNutK5Sap+5zTw/NV3oHsRpwBBg3TdRw7v0bxa1sYUaJPi370po7tNxbd2TzqUBFwB/Be+HywJ9tGGEbi8LNG7FEG0VaDoTa3XlbcLCy/nZZ59t3rXXXnvNWI+x3gKEybOMTNeYXYi2iLMlnzFyySWXGAAMCAYwW2GhAoFAjNhg4oURgDMx2oBd0jGxt7HHThdy2lrXZ44RW2/3lNm4bPbOcuq6qmxZtl5WLayWf72yf6tTaNCYt+1lufKfaSPkw4debbVtulXyULLy1fclEgprbGCWbKzOMypgwcApMBcDOlaUF+neJ0HNg7nsuWnOJml/vOal6RJpCMqAnHoZ1ko6KdKnjMyrkv45DRpL3SBr/v122usuUQGLpy2Uqq0xy+SyqgL5pLxAtjcEpC7sVaDhl5U1OfL+tli+1QaNo575mPtZTNRh+ONn1SLUMj5aOdpkdO8GGVDQHPMfd20kbCzBcWXuidHAj6+8VzYGA3LF/AJ5aXOmLK7SsLEKv/xtvYaZLeghSxevlZeecf+ek31cGtcukmhN/AI0nlvnTVog/3Pi+2Y789BFkq3A2CnR6u3Cta601EBUQxfqf71frML8aGNJt5vi4H/fLJGNn7a80C3pFBpo23TTKYbZtQfhskA3v3/tYUjeunWruYB4aAAs8bnE/5aVlRlSroEDB5p6mK1JzYSL8qZNm4zVlzZYfomTBgRzjnANrtSIjSGG9RmwjUBkhRWY+5H/lxhiwDZxwTfddJNp82WxQBNDbOORIU7rKrL2w6WGcGjt+l4y9W8T5ZsnzVcG76AElLzJq+ymwaB+vehvwadL+8tbM8aaaa1+1/0xcL6/lWs2sTrSVFQRzJZgpV9ZoGsl26+ppdRNt1EtbVWhDNlRlyPhaLPL8+aPlzddl+4H9dvLJeyI/R2WXyv5gbCsqM6VWmXT5pEELef6GmVYfo30zgw2qayhrFJ1Xi0ZBbHFh6aKND5Y+vYiqS3Hkh6TLbDD6pZMGkONsujN+XLmHd9KVp2WZVElY4ruWLtrc69Xz6MNuhA76phdu76bXhXSRcL/vvmR8craoouqT6zLaTnTylr568MvyRkXHt+yLs1LwqsXqGdB8/dee9URDYckvGq++AePb+8ladMusnqWxvkWitSVJ59zUL2M5j8nGf32SV7vlu5RDbgAeDep31oBXRboZkbqZKpNxpBMvK+TBdoCYNI+TZs2zVhPAa24I7/xxhtyzjnnGAIwACsEVffcc49ce20srgXrKgRa554bIxOxzM2AX65fu3atIddibFiYLXMz1t277rrLpF2CURpLLemYLr30Ujn++OONldf2tbtZoHHLhnQMYfzEMXcFqdlWISG1oIFyN2/tIQ8/eYT061she+mWoSC4sjpL1q4vlpraZhfKOjfnZdxbW7utvEWcUF04IBuqY4s1cY0TThoq4t0rE6rT6rR+a5l4EuKh+2QFhQ1XyZBJJxXRvMot1eIN+KVe3wcXADfrZseGHc0n7Tiq2eF+Fp1qilapa7NfCR93suQ769o81t+eaNm6NpulW4Ntm8tM6rK25l26LraA3la7dKuPlOtnElbijkpQMxRUbuvoVWnRPlq+Xl2zUnhyoAEWwrZ9nha66IqTdAHwbnrXnCzQiV0SP9pRFmhSEFkWaNIDOZmX6Z/ct1gwLQu0vaeTBRoQCAs0uXQhiTr//PMNC7QzD24iCzTpfSCkIh0T9wXYWxZomKDbOxZYoLHaOgVXZayqWDkhp8I6C/gD8AJmEe5nraEATlyPqQeg4kq+YsUK48pMSiiEcsvWzDnA1ebwBcBaF+rFixfLbbfdZqzEtANcc51duCCVEnHETqlT0iYIutgefPDBpr64BhZo6lOxQFNnWaAB3quV6do5R+d9rrvuOvn2t79tin74wx86qzrtcVTfw6rnHlMLZY6SZlhU4ZGNmwvNlmrg/sZ6qZn/keTuf2CqJmlVnq3kS1HNB7orklmQuyuXdctrILEKp0jH49dAH78JzEo+9XBVrZJgtb3gkPzq7lnao2/H9JHdI4k1rnuqpl2z8uT12jWwsbN3T4/+7bpPOjXq2auHYo22PaR69VGLnCstNODt0Tu2KJM0ZU+L5s0FgUzx5sfCHZoL3SOjgbze5vknVSwpqfg8hQNdZXVSDbgAeDe/MS4LdNss0KRlAtwCgom5xeIJ4RTCYgGgFwFEXn755SYFE4RV/5+984CPo7ra/tnVatWbZcndllywsTE2xmCbZnozCSS0VGpCIAlJCG/KC6HkDSEBEgLhC6GEBAgQSkIgEHoxHRuwwQUX3Lst27K6Vlrtfud/13c1Wu3Ku8YYSTtHv9HM3Llz596zszvz3HPOc37xi18YCynkVwBgQOU111wj++23nwHSAHfy+BIbjPszuY1tW8QIk8v3oosuMlZfUjxR1xJqEVd8+OGHG0B92223ydixY03922+/3TBVV1ZWCqAe2dMs0ORBtmLds+1+d11v+9dD0sdfrxa1HH0AJN9LXHvX/epnMuqhZ8Sb1W4ZTr6F3lWzYEi5hJo6xwkmM8qi8pxkqqVFnfpV6yS0m+EDIc1PXb9ynfSZ2P49TAuldTHIfQ4qldkPtOpvcGYXtSKHPBpTPXqSey86FeXJyBRPYX91g17lLE5uO7tIvK7LZCddZWf7ZcLBY+TtV+Z2OmYLsrL88oWzXddxqw/nGjZn8ev3NEUA7MnMlozB7m+jU5d2e81HzVKi6QsTvcrAqj3vP5tl8hfsGe66O2kg0cRFd+pjj+qLywL9X/N5HXbYYcbiitXVLoBRFtyOsXg6mZux8mJVxTJL6iOENVZsLLlYpInrxcILMEZeeuklYxmHHRq9Ax4B0FOmTBHirqlrmZtZA4hpE8AJsEawoiOTJ082zM+0gYWadEiAcqzEuEZD4GXbYgzOfn39618317JgmjhnFsD20KFDzbhw0WYcXI8x9mQJtbTI1r/fLYWh7SZONdmxQD40qk+1hDTuesezTyR7Wq+uF9q0SvoW1OkYU7MC+zR/Y//wUrUeK/2pKzL/xruVAZqZmBRmY4zedI5ez1tw0z2uFh0aGJN/u/izkr23PHLIfk9JuEXz2boS1UDG/qfqA2Y3JvnUoSZj+CHRdtyNdg388pbE6aG8XmUmVxK8b16ienelkwZ8wydpmIi65acqagH2VU5M9axeX3/LrPny7hV3yAfvRrwXYwfM4+iTxQNk2RtbZf6tD8Qedve7gQZcALwXPgSXBbqdBRq2ZdyWy8vLo8zNANXXX3/d5OglBhbXYQSwyDErAGTEuptjscWFmnRJTiG3MCAWV2VLKoVF2LpDUxdiLdy/IdJyCrmAbV1YoM8//3zjek0d+o3EsjhbN2pz0HHcWY9x2MVZbs/pSeumhR8a4KUflUwesFG73v4ZJRqHV8FvWW6D9FN23lBjg9S85LJBo6vGt56RUf23JJVHuV23YclSgqd+BdukZfn89uI03WqpqZPaJSslI4n7MJ6KOK96/hJpTeBCHe+c3lwW2v6RZLaskHMuXKC/kV1PzPg01v9LZy2RAf2rpW1lJJd7b9ZNKmPLPOxitbil6Bqu+UN9U88XD8Q6rnTSwOj9KuX1T+KACX0EDakcoGm5/qvvD7sB8jpdqfcVeDTVXtuEsyWopIDJCq9fbRO/qsB5154gybbZW+p9eONfzVC2bC6RV18YLzU7ctVg4pVWJWhrasyUeXOHycJ5w0xmgiV/+7e0Okgae4sOevo4Iuavnj6Kbt5/lwW6/QPCkjt69GgT/7tt2zZjKbWuxZBXITZGeePGjSZ++atf/arJ2QsgRQC4CO2wOIV4WwilcIsGQK9dGyETgSUaKywxu1yvpqbGuFHj2uyUVatWGavtjTfeaKzFZ599dvTwZ8UCHb1AD9lo/Hi+gth609u+uU0yfehqeW1Nhe4DhG08sDls/mGtLMlulsOHRD4LCpuXq/VSn649fTKgfZS7t9X83qtS4G+UsYM2yvw1g1SDnfXXseWIjqeNWineFrV4LHhPsvZJ79n56gVLxaNEVkzIePWeikC2XekRrYYNaDbn6QTZjoWfSNmUCR3VnYZ7oU1vqJtkg4wa3So/uXKW3HjdVP0tbdOXu/bXBYBxllqIjztppRx5jH6v1VgcXqeTWqMvTEONxR+yt2iA+M+8TVoeuED1s2v2XaVoE69a2/wn/CJ+g26p0cCcdz7Wm81+v/k9RDyyZUO17NheJ/kFKU46RBpIi/+bt5VJhqYk7NunVie3uh5yW5tHNmzuI75tpRIJSuu6fjodJWtAzdJV0SHX7MhTELy//k4GldRSw/8VBHd4F9KHzJbZ82XQUQdHz3E3Pn8N7OIr8Pl3sKf14IknnhC7/OMf/5Drr7/eWDqxeJ500knR4fDyD6BjgWSJ+FXAGgCRGFSsnOTCxQ13+vTpxkJ6/PHHG1BHIyeffLIA1ioqKkybXNMpAIuf//znph1ceAGGP/vZz6LWU2dd+oJ78MKFC03xBx98YKygxNdiDcWF+bnnntvtvjivxTbMzejjkksuMSReuBlfddVVUddny7aMa/Frr71myLvoi3WRtvHCse0yDoi1sMhCLIVYRmnSIM2dO9cAaepAKsa+tRDbtrAM9+nTRyDN+tKXvhTVN8dtvywLNMfRDe7euDVbRmnqYfW3YyS+mM8KpmsE4O8UYpuPOeYYs9g2nMe723brhnXqsdtuGeqX1yjHVa6Q8txGAexmeoPi37lkayqfsX2rDEjWW7JdQm0a+xpxZW8vTL+tth2Re2FIaY1MHr5GScVC+irXrlunRjJUt9mZrTJ930/UAqyIQ2NX26o2OKuk5XZAGaDDO8lxYHn2mokYXozty3GsWiLHqKdek0ZCwTYJaColV1QD9Wv0+x1hNh00pF6uu+k1OekLK6RyRLX0LWuUIUNr5OjjV8vlV8yWY09cHVVZuGljdNvdiGggcz/9zT8rMnHr+MnspJ56jbXe7qmQ3Gs+EU9G+0RDp4ppXhBurJa3H8DyxheXhVdYFo/4w03ywQN/7uA1pgdccWigfvU6WbJ0kGzchJec/gLG+YmkjGOblNBy1apyaVi13tGCu4kGmjZvM5OusdoIBn0Kfvn+7nyw7KzQ1hyQpo0uk3asvj7vffeXdg99ApWVlQbYwLhsBRBLzCnkS8ScEkeKAMbWrVtn0vnYugBULIzEtUIOFY8FGgC8eLHmXlWAB0M0QDmWBRpATDu0N2DAgE4s0IC1999/317W9IU2IZmCwMuyQBMTy0IZYJq4WScL9K76kogFGkBNDC1jxNJ7ww03mFy79MuKdVWeOHGifO1rXzNkVePGjTPAFJflWJdjADSuywBLgC2A0jIuW0ZpLL8PPfSQGSfjAlhj5X366aeNJdhem4kEQC39PPjgjrN1tl/Tpk0z1uR3333XWKAZj7Nf1ANY2zHyeUyaNMlci+vE9n/ChAlRMq7HHnvMdqXbrj05nWfYsfAeOWy1tLR5pTagcdSauzZbZ0MLswJRkOEcELGrXr/rVuXR+8xKeVG9HDFmuSzb3Fe21BQYdkmP5gEOq7XDr7ocXLpDKvpu1zzL7QDZk62kJmkuXiW+MebfnXowIFjf4tr0JUS110k7vJrg9uyckGHbvR93qsrX8ftdUNhqgK4T7HZSqhZ4vFnxitO+LHPsCfK/jx0jX548T0YP2q4uqF4NeQiZ73VbyCOtuv/krFHS99QfybDdiRlOEw2TWqr59mOksNajz5RS/X3saL9p03ee3I/vl+Y/PCPZP3pH70escK44NeDbGV62YlV/2bylWIYM3irFRY3RUIeQ3o87avJk7fq+OuEfCUXLyHG/104dsp3BMyeV7A1qbnefL7Fa/Pz3XQC8hz6DlStXdmoJoIPrLZZVAKYlUQL8AshgJnYKJEqAoWeeecZYPQHVyH//GyGWssRNznOInSXdEFZLBEsjFk4syhA1Acxmzpwpd999txB/u2zZsmiaIerTFwDuH/7wBwNysVzOmzdPfv3rXxtXYCyhWIdhRUaS7QtjcOZE5twHH3zQtLllyxYDPgHi6Ijrv/LKK3L00UdTLcrcTH1YohHiguk/1l+Irqxgrf7lL39pxkwZOrRkVOwDThGsx4BphOtNnYpbn08sozTlWG4Bv2+//Ta7cu655xrrO+0zmWHbYpLid7/7nQHu1ANQO/tFPRimSWlkxwgBFv1HnP1nn7zGVrC0d3fx9xugsW36ANDJhljx64sdbtG7Em92rhtXpErKKBsowfXtvx15mrd2wrANRn2B1gx9Oc6QLAW/TtAb1W2WkqqV9Yyc0dE+fwYb2X1LxKvfZacAaJVBIGrhAAYDfBGOxQo5hGnHFdVP0Si9MRUEtzWmpo7CfVKrnya1/RqT2qK6+e2/ciQvq0VGDayWorwWaQr4dKIrV9ZUFUiG/p5efdOYNNFI6sMMN26Xpl/rfalywqhc+cs7EaJMZ0v1qs+J/dQbZFudBP56umRd8HinHOvO+um4XbTvMNnwTEjT03qloTFbFi8lRY96wnj5hcSxq+OkgtcXlqLRw8wx91+7BnIHlavWkpcMDdEpHlOR/Aluzb2igY5vDXvlkr37Ik7QhzUSK+ddd91lXHxxd4YVGIGAiVQ8CBZMLKYAVYAgAoCqVAAMYF2yZIlxx62rqzOACuBWUVFhLJSk6QGUfvOb3zTn2X9cE0sluXdxhT7nnHMMqMVNOFboi03FA1D86U9/asAbwP2KK66Ium6n0hcsulignWIBJOAR1+T777/fAPP33ntPrr32WuMyTOyunSgA/F522WXGgj579mwDqLGsMiaEbdy7YYWm37h4o0P6DOAfNWpU1K0aoI8ejjjiCOP6fN111+nLcbiDizNu1rhFI7fccoswqYGVHcB7wgknRPv1wgsvGJZoLOI2PZOzX4wTkA/oZYzELFs3d7wAejoLdP5B08R7j19gg95doQ1XRHIPPVlaFs+VcHNnsIGbs3F1TqAoXCWzJx2R4Gj6FJO+KKQTafHEgt04mLdD9bDm8irZf3SHsnTd8fY/UpoDGsqQwttBoDlTcgaenK4q63LczY0BWTF/rXHUbQj45cOV/TrU54kcCgRl08qqDuXuTrsGAv+6VGdm1KIbbpPxAxrlL2cukW89NloKs4NqDQ6rt1FY/nXux5LnV+8YjbcOrX1fgrPvk0wlFHOlXQNlee/IkgzlSejwc6lBN2r5jSfE+pflv6OHvh7vcNqWedUgMvTEw2Tlv1/SaJHgLvXgLy6UknEjd1nPrbB3NdBxumfvXrtXXg0AZxfcXon7BJCRFoeYXitYIW09Uvjg0oyl0VovAb8I4Ja6pA0CkALgZsyYIfPnzzdr4kzJS4vLsxXqA2pt7G///v0FgimsuZs3b7bV4q7pJ5ZX2kAs8RPbqfQFoM+YnAvgj9RH77zzjon/pQ6uxgBCxoHVGmHiAKE+8basjzzySMElGuEc5KmnnjLjwaL6l7/8xVhrGSfAFtdwBMs4EwaFhYUmbhqgjA7ROdbnMWMis+64UM+aNctcg/a5FrG7xG1bizApkugLa2caJOsqTT5jhGvweWDRtmmQSKvEZ8Ixq1tTuQf+y6oYIZlYgXdTvDl5UnzK6bt5du86LffwGRpL5N+tQfn3mSC+/pHJoN1qoJec5FWklj92dILI6V0PEgBSuP9Y11q0U1V11X5ZuaCPBFvjvxTHajSsj57GWr+sWFAce8jdVw38/Rf/FJ8/I3p/Oqeg7TZP77/97GGpqap1dRajgdDW5RJa/roBv/bQYcPr5PXvfSQ3zFgpfzxtufz3WwukNM8BRAJ1Enz1Jgl3RHr29LRch5u2S/aWJ6R/5Xb9rWt/X+xKGf0rt0n25id0gra6q2ppeWz/y88Rf1FBUmM/+Pof9vj3vqQG2sMquQB4L3xg8dIgAZAeeeSR6PL3v/9drrzySuOGC1gDHGMZxtoIkIYgC1bkQw891JAlkZLn+eefNwANN2viUa3grnvxxRcbkGxjSnGFxlU3Nv7UnsMa8Iu784IFC4wrMHG0WDDnzJmz231xts82LsFYRg866KAOh7AY2zHgxo0Qv3vnnXcacimOAWoBj5bZ2YJ5ACjH0Cd6RLdYZhkPYJaYYFific8G0GMlZkwAUlIpIegZyzOWXEArOkSwGjMxgeDmTJ9o19kvC5DtZAFEVvSTCQN0ST+JN+Z6iQi8zAV60L/yi36YkguQHRovfBklpZJ3QMfP3x5Pt7U3r1Dyv3SRifdNdezFF16R6im9tn6jvzjq7pzqINX4K41ZRame1mvrr545V954aqQ01PjhWetScCiC9fTff9pXPnnmvS7rpuNBJmNf+fubEmyJmNyAHfwGxi7oplWtwO/+Zw6brjg00Lb0ZbXqRkjZHMVSnt8qR42skYOH1kcsv86Duh1uaZRw1dKY0vTdDa3FKOCVcYeulrzCABpKqAyfPygFfRpkv8PXaB21EK+JGBQSnpCGB3LKS+Wk/0a8OINxVMlvoy8/V6bf83/S/5CI8SYN1dSth5yCk1O3Hke37ly8NEg8GCFJsgIohDwJkHT55ZcbAEZ8L4AJ91vImrCUAvawaJL+B8svJFL33XefYZ62Maa0CUMxRE/WFRpQh4vwV75Ph/bLAABAAElEQVTylQ7WYnt9C35xO8ZyDCAFuBN3TFzshRdeuNt9sddgjSsyLsEWYNpjuA0Tgwuwt3mAAaeQcj3wwAPGcorVFp1YpmQbL0u8caxgbQes0hYMy+gbt+Ynn3zSgFP2cRu37si4LJNPGEIwziH9Ub9+/YQUTLiSIxbgYhV29ot+AqCJxUYYIxZtzofpGiGGmn4wacEYsSJbgS0cCzTC58153V2yR6nbqeas9AYDqs/kessDgbrZEw52rW0OlWWMnqrkYbnqxtfgKE28iR7rmkpkSIUbM2i1VLe1TnY0Z0u5krElez/aczc35eqLciStly1L5/X2ZeukeUebPHjDJPnq/3woeUUB8Wd3thi1NHsl0OST/9w5Tprq/bL9k3XprLa4Y9+2frvAMB4rcd6XJdAQkEVvL5UTLjwytnpa74e3LtPZgV3zSnRSkromhKvXivTbt9OhdCwI16guWurMhNVhZyyUxbOUjHVJhCMl2BKxhflwIVcZPLpKRh+8PqKmlnoJ1+q5rnTSACD4wAdvlr+d/r8yMlMJP7UG322yC2xSRuiRZ31JBh09pdN5bkH30IALgPfQ54DLMuDFGQOMNZFYV6yTACKbBgkWaICWU4hjZYF4CddZBPdhziX2l7jZ0047zcSlYsUlDRLEVVgXqWOloiLCAg3Aw3pJewBa4o1xhQbYxrJAY0GOTYNEvYEDB3ZigU6lL7ZPzjUgt6goYm2hLYip0B3WUgQmZ+s2TEoiLNDoasqUKQboUwcCMcSZ+gkXY+KmEQA7unPKscceayYYsLKjGyzspFmyAgBG/1iP+XwAwwBs4qGJDeZ8+o7w+TCZ4GSBxn3aAnPq4XodywLNGAHAjBHdWgH8Wuu3df+2x7rrOqz3tvjzTBwwBBq7Ah2ANqRFiZ2It3SlXQOh5npp1FurTScUcnIjEwrx9Km3pj5dPVJblyNtGgvHfdzT3enbtfDpttpagtLY5pfNzR7pn9NkrMHxdGivwv2IOrcqaA6EfBo22NnCZOum2zrYHNFFWOMC//G7iTJ8v20y/rCNUjZILWqqN6/GENZuy5LF75XLx7P6SUtz5DXC1WHnOyXQ1JrSZF+gsaVzI2leEtZJ1t2VT3Pu7l6z257XpnokXmGnjJmyToZP2CjbNxRKY10kDCe3sEX6DKjVCS/npI2e8yk+A3u93rpu9WfJ662F8tSmOinIUA83jUmv0xzKPmWKvjS3sLcOu1eMywXAe+hjhDAJl2VS7cQKMarf+c53ommQsBDGE6yiWHMBzaRBsgIJE8AIF+YLLrjAFN98883GEgwIxDJqBfdhJwv0d7/7XeP6++ijjxrrbzwWaABlbBok8hH/+9//NizMThboZPuSKA0SVm6AZixDMtZRBHCPdfSiiy4y/bYMybzoY5klztZpPbWM0sREI1jHr776ahMzjc4R2oT0ir5b4fMCfOPKzMQA1mLK0BfxvQBf4rGxzEOERf9sGiSsy7Es0ABn616ezBhtP1gDsK2Q87kniK+kj8njGw5pbJs+VDOU/VknPY04gYcFvpBstCnDpEI28ZVGZp17wjg/6z7yguZ553/MtHFTU5Z6ByiZUE6L+LNa1RskwmKMPts0L2OzHgs0+xWEqA5zghKceaVkHnX9Z93FHtF+fv8+svmj5dLUlikbGj3SR9NvZWreZO5Jm+uXgTD3wj3ZGs6QbYFsadH7F+F8VyIaKBpaLt5M/V636iSLfm+Xz+trFtEXO39WWxTwxuorf0BpbFHa75cN6aPfXSeYSKwSn98nw/YbnLhCmh7xFA/VL7G+qoZ00jUV0TRInoLyVM7o1XU9eepZ5tcMGmoFtgLQ7T98F/G9mXniye/+Xml2THt7PXJYlmRLvdTo06bOfNUjb0JtOpF4yGQXAO/tzyOV67kAOBVtJVGXvLlWAEXE7eKGfNNNNxnCJaybCK7OiVigE6VBApRZ+fGPf2w2Y9Mg2ePW9Tk2DZI97lwDLp1pkLCkEvdK/7GY4oJtxVpqd9WXRGmQcHWGzIu2YxmSuYZNEQRQR4YPH27ieDkPgIqbMeDcimWUJvbXybb8+OOPG7dtADGWXNi1nSzQWImx1OImjrsz7a9Zs8YQZF1//fUC6zRg+2i19jKpAdCmDoLrORZnywIN4Abw274nO0Y7hp64DtbV64tdKOLyo/kYg0GPsQJ7PDuBMM8AAzY0fkgBG3FEVhrXb7Gbab9uW/SYZIZXS0amTiiodZw0FORfjORgxMILCI64p0WVpTouGNAgoXn3SnjSdzRtzbDooXTdGH7cgbLixfeNdwEW3Y3qmutXAJyT0arrkPhUj0G9D1tUv4BkC3zRl0cR8vDjJqer6jqNe9BBY8SrYKN9SmtnFfS309obexJMvMMO3ie2OO33SYE0/sh9ZfZ/lem9TX8Qu5BMBcCHfMm9D2NVlDFqurS+fqt4mmtiD3W9rzNd3sGTuq6TRke9w6abyZjIlF/yA2cCxz90evInpFHNcEuDeO6dKn/+Rki+fOsUycnUiWnDqO2Rn52yRCreOFVCw18U75CpaaSVnjPUmDerntPx7tpTrLF2OfDAA1NmgQbEIdad16Yewv2ZY8QAA8oAdsTl4mZtQanVCYAWgI3rs3WFxrpMbl/2Y4W6Ng0SpFG4KGPNBKzj+msllb7gQg3IdC4AQ2KNWbDwWlfnH/zgB8bSypiIycU1mlhd9IhFHIbn3/72t8ZlGeuxjR9++OGHOzBKw7BMWwBRLLrE7AJM77jjDkO6BXu2ZYE+8cQTzbAgqULKyspMuwBbmKptqiUbI8w1bXomxuFkgcaVGr1aBu9kxmgu2oP/bbj/AWkNqjtz9Hby6DbpFDLU0quL5q9lHTLgrR38Ygne9tpbAoBOd+GeaZv1e/G01kvZvtVqdYu1FKHTOD/RWlY6aoeaMRslOOeOdFejGX9+qVo22m9GUwbIrWnNlqpArmxszjNr9p3g11TU8wo43xWjAc+aj82LnJnBSlInXp2Uyd8U+S1N8pS0qXbBjV/RSdyubQ2Z2Zky6YTxMnSsawGOvTEyhkyWHS2pkdS1qMdMzdBTNXNSJGNEbJtpuV8wWN+JivUZnfzoqbtuvXrHFAxK/qQ0qRlWYraWuxTYKuN4Rd96efV/X5PLT/5Evn/sCnng4tny5ckbjCZaHz1bwjXr0kQrPWuYcd6uetYAekJv47FAJ+q3daMlLhYB3AJoScuDa/Ppp58u5513ngG+pOSJlwYJsOZkgU42DZIlwiL2FmIn1n/729+iXU2lL4BbZwoktukvY0Hsmm2AgF0ox9KLOOuwb2NwrY6effZZiqO5ftnmHAuQibPF0gwIdrpNU88CX6tnCMRwM0dXjNmmR8JCTD5jQC/kVUhsv2x/zEHHcWc9Oz7WznJ7Tk9bb3t1ps4maz5BBbQxuKPLoQCasbjVfDCny3ppcbB2jdIPR3J/Fldo/JBadXcpamkbevh69WRTd0CNAw598tQuT0mHClXvfWwsvqmAtohe1K1XLcRb3luYDmpKaozbX3hRRhZuM7FsSZ2glUYWVUvDB7PVAp/C23WyjffweoNHD5TbPvy1GYU+6TqNBtfnySftL1f884edjrkFEQ3c+vL+0tya3OsqYQ5+X0j+Ndf1SHDeP1uXrpNXZu6nBojkJwXUNiKvvDpWtn0SAXPO9tJ9O7z6DU2dohPRO7/TRblBA3q/edgaGdXf8SxXErHgnL+mu7q65fiT+0Xpll3vOZ2KxwINMLv33nvNAuC69dZbTUwqRFqkH4pNg0TcqWWBBrTBAk0eXIi1YtMgoRlYoEmfhCs0rr27SoNkwS8s0FhtcYFOlAYp1b44Pymug5UZRmabIggma0AkCyCTuFuEvrz88suGXAo35rvvvtuU0wZiXY5JbzR37lwTx0xbpDxC0DFkVMcff7whCoNtmb4T07x8+XIDyMkzjJAbuKIiQiAG8MeCDHDGXZtYYcSyQOMO7UyDhPs1VnQryYzR1mU9b94846KNm7YF2c7j3W2bl9yWrdu0Wx4JKNMhr3RdgWCOsTS1+IxFOFjfIM0J4uC721g/y/6Ea9YwmxK9xKCDqqR0H7UE+9QSrEDXKZRlZAVlyLRNkl8euf/N8Yau83o72+jN29s+XiHZ6vIMAYl9Idn1eNVFUu/ebG9QON+ViAaa9LcxxxeUCWWRe0unuBKoJqz6DsnI4m3SJ0fvSf2SBzTkx5XOGgAEH3zmQUJUOiDY/mmUtbToPfu9Oy7ofJJbYjTQ3NQi733sk588fNAuNRJU8qHqBr988ZZjZfYb+vvqSlQDVYvXqRElT0O6ppiyXT2zAwGf3P2Xw/R9Kk+2LHJ1GVXkzo221W8qAE7CLb+tRcLLX4g93d3vBhro2i+nG3Swp3TBWgGTYYEGmGEJvOeeezoNj/hW3H2xYto0SBAjwSgcjwUalmcEMiysmPTDWhtjWaBtGiTbV86zfYnHAg0RF5ZkYlwhlAKcp9IX2o8VrLhYxHEXtimCAJ6nnHKKId2yLNAW3DIxgOUbKyygH9BsWaAZJ67KnIPrM4Kl98gjjzS5fi3bMoRbEGjx2XAOgjWatq2LM1ZZdEz8L3pCL4Baxo/eEWuBBjTbNEj0E/AMi7OTBTp2jLiYO8foZIG+/fbb5dVXXzXXoB82TtwUdMN/wZpa8SrDYZtazAHBzS2Z4stok0wlwoonxAC3GHdpb+Sw6rbVAOh4tdOnLNy0tdPMQb/x26VkeK3UrMmXhqocExfsy9acjAMapXBwvWTsTFMR1ZJXSbGaq8WTHQmdiJan2UagutbMJeQrcKvTSRmsQNybiSWsccEhydOJBeYgmrfXJq6aZkdCGmaCZKtuDizfKGvqCmVbc46ZLIDEDghHfHBeZqtUFNZIgT/CXOzRZ1ZQJw2VPCHNNJbccBtqm0VfhTtVLirIkerNNVJSnpqbb6eGemnBjqpa8akL+cINfeSS+6bJj09cIP0KmyTHzzMnos8mTeMT1Pj+uatL5ZYXxkm9EgbW7WjspRrZvWHVV+3QdMo62Vefr2Flh6nxZL4aCBrUINGmIXURPcLl0apcFNu25ctzz++nnCv54lOirIatSQC93etWzz2rAS6Tzt/neAMKN2EpdqW7acAFwHvoE7Fut8mwQDuthc7L0wbWQIAY4NOmQYKNORELNCmQnGmQaAOA+89//tPEqcayQOPiu2FDuzsLfQHsJWKBpi3AL4RPLMn2BeupzYtrxwhYhSGZuGXGCLAFXMN6jbUZ4XqAWGJxmQiwApkY7QEcrTszbQFAscZynBhnQChs2TNnqouutoVQD4uuBb+UATQBupYFmjJikFnQB8J60aJFph30SjsI7RJLTDwyYByLMG07r8cxQLfVL+zczjGahnb+A3Rbqzaxxd1dMkuKJawP0nbxSLDNp4ta1JS5GEIcHgyRmOAI83N7XdW96jhrQH9nUVpuG1ZOz85JAYcG/HlBjQneYRZHcfxNJStKd/CLYnL7lcr2RasMmC1QENyqL8PNGoceb0rGWH11ssbvmLDJG1AWX79pWJqhv6mhnV42WQqCR5VUy/DQDqlvzVS9Zhh27VzVceyEV1h/FzP7uEzQiW6ZPqXKpquTLTvnYKPVajSHdd9B6T2BFVVGnI2S8kIJapozZMmmYvnOvYfJmAE75ICh26RfUZO0tnll3fY8eW9lX9mwIy/aQlFpQXTb3VCPubJiTc2TKW2BVtmmwPaBB6dJv341moZyqxoCGs10YfWOXFm1qq96FbZPxmSoi35eWfu+q8uIBjxFQ5NmJ3dZtLvnXeMC4D38ubgs0NuN23FXLNBYcbGmAoJxc4ZtGesyYi2/FWoVBqRiSQXwkpt3zpw5xto7YcIEU5e4XNoC3GJNZ/IgXlvJsEDTIOmISKcEs/TTTz9t3KqZXCB9EkzYXA9JhQU6lp2a8+0Y2UZw07ZiJ1LsfnddZw8ZLI2fLIvpHiRYusSUxu561cU9d8Tw2OK02/eUjk49tUeMljzFlTEl6blbfuAYWf/mhxJWxlJABuA2U2N7uRfxQGBhYsYsWkYdK15/ppRP2sfupv06b9w42aHcB07J0ImtoqyIpddZ7tz2amiLO7Hl1Ej79ux/vStLn57VCfxSo2+2Ry4f8T35f+vvlqzcrPaT3C2jgSxl0h6qLuRL5qyMamTxxmJhSSQ+TeN16IwDEh1Oy/LBB+0j4RZH+IxqYfPmIrN0pRDO4VxXOmrAO+JYJbG8bWcccMdjHfYyc8U75tQORe5O99BAZ/ND9+hXj+2FZYBm7bJAd2aBBkRipf3GN77RgW0ZKzBWVeuSTFomiKgAkLBAX3vttYK7NzGyFjAm01ayLNBLly6VWbNmGavwlClTTJwyQJUY67ffftvcjy4LdPvXsu9xx4pkRizi7aXJbeEqWbD/+OQq9+Janhy1luV+CsuPgjjP8KN6sYaSH9rQYw+WzLzsDicAcjN0yVTwlmUAMTGrHcEvJ/hy9AX7mIM7nJvOO2Xq6YMVOBXx6G9Bqf5WutJZA4tf/1juOFdflNtCMixXJE/z0HAf+nXpqz+hBerGG1af/T+e9XvjSdS5Bbfk7B+cIFkpmGsyfV6Zcd50V3EODRQUhaRvH1yZk3PbjZwalr6lGuZQlMo5jov24k3vwEniHXSwajMxjDLeHpqtIeOAc3qxJnru0BJ/cj13TN2u5y4L9FHGnZu4W+KUAbGwV1u2ZdyHiXcmxRNWX0islixZEmVzth+odRO2LuTJtIUbczIs0C+88IJJc0SKKacQRw0hGGIJquijU6zrsy2zx+2acsZoF2e5PaenrQecfcbOfHep9ZxcrCVf/JJ4d7qTp3Z2L6w9oFzdqHZzXNyH/Ybs5sm967TSccPVijtG056kpkzqD5y2v5SMHta7FPIpRlNy+GHiLem7M446uYbCGtYy4JvfTK5ymtX617WPREesBPhSrvM0QxUID9KlYCchb0jB8eoPV8kn7yyN1nU32jUweUwfKcsi8jw5IDapqFH6DUxsIW5vOX22Qp88K0cduVw96pzhS7sav0eOnr5cQksjGTd2VTvdjlf3u1SCATzfOo88pIRsIXXP39j/Dxqm5N6LnTX0+Zek9rbw+fe3R/bAZYFu/9gg+YLMivhfSKoAtQBUYma/973vmYo2Rpn4WycL9F//+tdoTC0Vk2krWRZo4nhHjhxpUjDhCg1bNFbpd955J9r5z4oFGgDd2tpqFkByT5AMtdZ/WDNAjRodJwO66jvEREHNYbsd119XIhrwKhFWTgqmDafe+qq7ZO08Z0nabjOpdMRNPxRPChMrfNXCCjwOu/HStNVbvIETo98wcpq6i3eOV41Xn+/12txx4i+LhIjEq5OuZY01mo9+0fqkht9U2ygfPeumh4unrG2zPpSzBihDvt6Tmn0+XhVTlqVhD/sXNMlRg0NSPW9xwnrpeCC0/EUZXL5Jjj86uZRvEFueNmOODNJzQstfSkeV7XLMa//9krz+z/Gyan5/aW5UjgQlY2sNZOg6QzatLJG3nxwry/721C7bcSt8PhpwAfAe1jtszHYh7Q4ER1g6sXjiTmsFoEPcKgvADtKqtWvXGlB32223GesnIMwyL2MhJZ0PJE3IySefrGQFq6SiosLsc02n8EKI9ZKY0t/85jeG0AnSp3gxpvQllgWaVEUAQdakRSKOdnf74uwX25BboQ9YoImxBfxdddVV0Xy+lm156tSpQkw1KZ3u1ZRRX/3qV6WkpCTKtpxMW9SBBRoCL1igIReDsTmWBRoADOAFhGOB7tevnyHr+ulPf2pigGnH9suyQNMvdHPYYYcZ1+1YFmg7xjPOOMN8VrBAIwB/p3BN0l6x0I+eIG2tbbK+LltmV6kFMwlp0dnQHS1Z8t/VQ6W+CjcsV8Ihjals09zS/dUUhE9kskLVvmpGKsqScNOGZM/q9fWWz/xI5m9T/1KVCAt04iEr7pVGvScX1mXJJy/NTVwxDY+EGpXY74Vn5f2NA6RRia+CCSa5mPxqUQvHom1lUr16swQWu3qMvV2qN2wXnz9yT8Yei93HDXrLikjqqdhj6b7fsH6T+MIh+ZESNk1WwqZsBbo5uviVyZ2F/QIFbEeV1suJ5fVmYqu5qjrd1dZh/OG6jWZ/7OhNctZp70lebkD8mZ2twZlaxrFTZ3woo0dF3kfsuR0adHekYeUanSX0yMr5A+Stx/eTd54cJ+8+va+8/th4WfTuMGlpUtKx5oAE6xtdbXVDDeym6aEbjuRz7lJlZaUBNuSktQLYLCsrkxNPPFG+9a1vGdDFMQid1mke1DPPPNNWNQAVCyOES4lYoAHAixcvNmzDMEQDlGNZoAHEtIPrL6mDYlmgAWvvv/9+9Lr0hTZjWaBhUmYBgAKmSePjZIHeVV8SsUADqIndZYwAapibuTb9smLZlkk/9OMf/9j0j3hqUieR+9jpckzM8I033mjaIrfwypUrTf5j3JUtWzR5hQH4559/vrHoAmQB9EwQ3HHHHQIzNkRanEvao8MPP1zq6uqM/mDARs/HHHNMlAV62rRpJu0SQBprNuMhz7LtF/23TNdYs/k8yMkMsRZi69nxfv3rXzcu4uxDwNUTpEkJsEKq4/XhfJm5MUMOKK2SbFIh6YsIs/RWWvUlGTbolXUFsqC6VF3YRHa8OlPk8i/ZKmm79mgKIw3+Uxdo1UplgTKSaPqZ+lYtS6ASlMdSquC3WK2/iK+d9TRSkJ7/V74+T5669P/pl8sjj23MlQOLWmSQpo9ihjfTMc3bqurGW21tk0/m1GbJfvlt8sL//kWKh5TLkKn7pqfyHKMG/G688HDxS57qKU8WKrgtyW6WvjkNkq9pjyAR4/Zs0lRT25pyZGuT1lOvjrysgFT98nzpd8Njkjl0lKPF9N7MKdRUZkHuuOQkryQ/uYppViurpMiMmHnC6aWNckhJo2zUVEf1Qa/OHYalRFP59NW0SNFnj76zZBa6v43O2ySc0a6PyoptctF5r8vHSwbIkk/6S119hD+hUK3n+4zcLGNHb9T3nUgWDdoIu88Zpyrbt1t1AtshrZo7OVbCgWbxKpGbK91PA50/re7Xxx7RI8BTrAB0Nm3aJAsXLjQgz5IoAX5Jn4Nl2CkAPBiOYVD+yle+ovT0lebwf//7X7MGRMcKlmHSA1liKKzCuBfbNEgAs5kzZ8rdd99t4m+XLVtmwJ5th74AcCGdAuQSrwybMsCUfLQff/yxAY9jx441pyTbl0Qs0M4ct4yXWF/AslMAlAhWX3SH0EdAMqDZyaLMOP/0pz+ZOvYfjM2Ad8ZkhXoPP/ywMh5GZtgBrlhiX1O207PPPtuA2NWrV8tjjz1mQLE9j8+AzxDLrO0XkxQAVdsW13L2i3offfSRsTwz2cB9AAgnZhlx9p99ALcVcgJ3d8FjYNEvfqU5VFultjVLtmqO0BfXD1WjZJP0z2nUuLYWA4Qb9SV5S1OubNKlRdOnID6PvqSsXSrb35ktfaYd3N2H+tn3r01n4A2w1X9YggP60lGtD9UGLcdHV+97s1ZSF8nXn+sSBb42zlWPhxtda3pYJ/ue/Z87zGdVpPksmzQF0sztOWoRCslABcF99eU4V4mGsPhWaY7LDc0+qVfLpU9fnKkfUNfTZy7/s1z05q3md+az/9C77xVqH79LQi0BKc33aGqZYrX+Zki1fr9ZEosS5eQ3SLi+Vrb/+Srp95uHE1dNsyPFA0okS8nZcIXelQCWxxzuTsLE01OfA8aKLz9PLWkN5jCTWkNzdLIwgRCTXjzWnYhxqqd2yTbJVxymmS+NAHAnjl9nFme92G3iW+uXbJWy2ANpvl/33rviqVqlWgDc8hCPJ2HJ9LTK2l9fI8Ou+XW8Cm7Z56gBFwDvYeXjZmuF3L1YAO+66y7j4ou7swWAEDlZ4IYLMCAQoPrKK6+Y0wFQgC8AKyARd1uskgAq3KArKioMaRSACVD6zRgCEq6JpZLcu1g6zznnHANq48WY0hfALwITM26/uA0DPq+44oqo63YqfQGsYoF2igWQlGGFvfnmmw0IjXXLthMFgG/6fsQRR5iURNddd52xrDImK/QJC+55551n0hOhb6y49lpMNDz++OOGSZrcwlixsTjjcg2o5hoIABWrOZ8Prt+sid22nxF9tAzVEGY52/rFL37RoV9cG8BMm/fff79x27Zu7rhe23bsGHrauuqlmRLYUiWDcz2ypFZdfNQChACEWboSLEhFbTtk2e9vk4P/+feuqvb6Y6FVSizSrIA3eyfQZcRZOlEAEEbUJVKVy6xBBAhHStv/A5A3zZdwYId4stKXZOPjJ9+Wpup6oxe/3orl/pBah7xSpyB3SYNflrRrLLoFmc4ArWetww1VO2TJf2fJmFOmRuuk20ZIAWz90/eLaNqT/sUtMn+d/n4nYbz06He6omy7UVfrqqXSPH+WZI+fkm7qiztenq1HX3ycPH3DExJo0ImtLoTn0YSTJ3VRI30P9TviIMnQHLbByNe8a0WoHksnj5esPhGrcdeV0+No0ydqaPi4QfL20x9I/d1LRcJqZa9eWC/5y5ZKzsh9Ujm119Zt2bhBVv78MsnzN0tjs74DGZXGA8EeKVSjQM2bM2XHzJel+Mhjeq1OeuLA9Nvgyp7UgI3lZI3bK3GfgEjInojptcLDztYlZRIuzbANYxVFAL8I4Ja6pA0CkAIGZ8yYYfLVsibOlLy0gDcr1OfBa2N/AYjEmQL2rNXS1o1d088HH3wwagmxxE/US6UvAEjG5FwAfwggHsD+1ltvRd3CzYGd/wCbuBFjKWWMgHLGjdUWmThx4s6aYsAsVnPqkQMY/bFNKiOESQis37R11llnGVAKeRb9YjLAglH0hRx77LHC5wEIxwrPeeiTfVyqGQNrZ1uwVyNcH6G/fB4wSqMH9gHMXINt2uvJUvXiqxKsrZNh+fXG0pv8WMKyT2GNcVNrXrtBAlVbkz+1F9YMrXhSp9b1jU5xbFzBnw+Eluh+UXDsaVUot/71uKenS+Hip96RQE3EMsSYx6tbs2pGt+IrFvDL8fEFOvmwU5p3NCgAftfupuW6eb6On0kVlUy1nu8/ZIN+V9ufK/GUwvFhpdVSktdkDocbaqXp3RfiVU3bshMuPVlaPF6dz4p/P6IYjpz1xwskp6DrCcR0VaIvJ1v2v/K7yQ1f9TzxGpfYzqmsHa+9Ig1bPNJc5zdRN85jXW0TodNc65fGKuXw0DZciWig6p//0JzKLeZdpqywTgs9+uf8fpNzXtNOFeg7kv6WhvQ5v+kvf3bV18004ALgvfCBxEuDBEB65JFHogv5bq+88kpjGS0sLDTgGMsw1kaANARZGzdulEMPPdTEoxLj+vzzzxvrLC66xKNaAUBiBZ0/f75x6aUcV2hcdWPjT+05rAG/xMouWLDAkDuNGDHCWDDnzJljiKp2py/O9u32s88+a8AsLuCMNVawcDMZgMX7zjvvNKRXjI/xEENMvxDALW7LFhjHG5slDaMt9I0OicUFfCPWdZw4XCYTyDnMtXANJ/4ZCzKAlbZxcyZWGD05+2XzBNvJAsiwOIcJA3TJpANxygBg2ujpUjPnQzME4n0P7rslyeGEZUR+rYa68rDQx4W68dZ+tCDJc3tntdCmd8SDlbdRybBYpyp1alFqrZXQxsi9nOrpvaX+hg+WdhgKcwYn921R1+ewcXP27nwxYY3bc5k/LCfq8Wi84M6z181Ob9bYwILZ6lIf+X6iksGaM/TAyrVGO5rQY6eW2lc+b5sMUfA7cdiG9kJ9Y27+6O32fXdLqjZUy7ImDRfRCauggjMnEGa/Rb/7K5qD8spTLgN0V7fLoFOOktdbI4648eYSCLUmzOHjicdK3tCBXTWVdsfq3n4D8hHZ8GG5xqQn/9qv8zayfm65pm9oE9NG2mku/oBrdDIAN3uE58iA4hop1knAXH/ALEW5TVJWWC9+X/ska8umjdK6vSMBavzW3dK9pQHXBXovaDpeGiSsjxBBWcFdFqsnIOnyyy83bM3E9wKYiGkFtGGtxNoIqAP0Yfm94YYb5L777jPM0zbGlDZxmSa+1bpCY4mEBRqrptNabK9vwe+HH35oLMe4MAPciTuG6fjCCy/c7b7Ya9j1j370I2MJtfvx1lwfN2xckB944AGjG0DoBRdcEGWyJu6aMtygIfsCIKM/6sMYzUSABaVYdWnL6hyrMUCV9EvImDFjDBs3rN3oCR2hb9rAYozObVuwef/rX/8y18GiSwwvkwPEYiOAZ8A0TNIwXSPEUEOkxaSFk6CLY5B3zZ07l00Tv8153VmCde1+aMX+FjluwFp5Y8sAjRf0mjRHHfseAR1D8+pkXEl19FBboEVatrfvRw+k04a6LiOehlYlGclQV+gkf455+9vRrNbfCCgJ169PJ611Gmuzw/prD/JSMqUoqG7QHtmmVvKArrM0DhhQnK/reEb1eO3Y9tJh3VbV+T4aVFIrhWOXyrItGtZRk6+szxni04mvPvmNMqJ8m1o42i3vVkehush9bffTfb1S0yB5lftgjcb3Z+l9WaCTf1kKLIhuaFTwW6cbTH8t+mBZuquqy/HXaxz13JpsWRUolOl9GqW/5rNtU4JF4Bz6W6rhDq9X50hpbrKTsl1erlcdDO6IPGuDyn+w/LUhss9xqzVHrYI3fezEE461aSqfVW8P0nXkuWTbiFc/3cpidcHzJMffapZEuiC9XGvVFsnsU5qoilu+lzWQ5BvXXu5VD7xcZWWlAYjOGGCYgEmtA1MzgMimQbLMy85hAsRYzj33XOM6yzHcdDkX6yWkS7AiW9BHGiRInrAuUsdKRUWEBRqATfws7eEKDfjDFRpgGcsCjQU5Ng0S9QYOHNiJBTqVvtg+xa4BjQht0V9AZjzBag0Iv/TSS421d/jw4XL66adHqxLLiyxfvlxITYT78pNPPmmss1husYLb1EVf+9rXTGwzbuCAX0Am+rSpi2inqKjITCgwGYBe0TkW3B/+8IccjraFazMg2ckCPWvWrGhbXBPLMmDbyQKNRRgADPkWurXC5IclxuoR7tG8bTgkR2c5j+6/XtY25Mv6xjzlb/KplUNZOPVFuTynSSrz65QYKzFhiaOptN301DTrjHKm6PQx3lS68C9GsBIDfmsD4mlpn1mOqZV+uzH3o1UAKixUkiuWpCTJakm11RMrxbvndBwFOQE5YFhncJx4iOmuyI6aaVn9qKIJJlozJKCqCSRghQ7Xf6JulRrP7y/u2IC7F9UAqaLWaszlAxuKlFyIyayQ8EtYa6yakd/MPvxOupJQAwDbpS8Ok76jqqVokKaMUnV5vBGdhTVrAz8DNevyZesnJdKmpIGuxNHA7txiRtFxnutxmneL9o4GXAC8h/QMMMVl+aGHHurUIuCGdDo2BhYLYTwBCGLNBTSTr9YKrMO4++LCjAUUgUAKqyT5f7FuWollgY5Ng4S1FDdeK/QFsBibBgmr57///W/BIgw4tizQyfalqzRIEEThEmwZkgF9kH8BLGNl5MiRhsQKYi76DpDF/Rgwj2v41VdfbdzCiculDtZVXKRhfKauTalEXDNjQbgeAB8rbzy3aepgWceijHXXulzbtojljmWBdrZFPSYzsGDbMQJysQAjsdfk3rAyffp0u9lt176iQn2fi1jObSd9+gDFvdm6ONvyROuM7Czx9ylJdDg9yrN0/C010bEaS3CTulXlKRDO0p9mzJg8aHlm8tKsbpKirpT63tdBPPlDOuyn2052cZ60NnVNMJSMTrLTPAVNRln7pFwy+kpUJ6OwT6JDaVceXPkXqcz9lz5rxnU5do/GC44bUy+Bt2dI1iFPKwhO89/GONrKL8qVgryQNOz00m/VSdbqYEeAhh73P0Bddl3poAFfSR+BuMkKIHjzQvXqWNxHckuaxZcVmVANBjKksTpb8yhjV+8otOFKRAOZpX0l0NjZ+6Ur/YTVW9Hfr39XVdxje1kDne/yvdyB3nY54kbtgmsrlkLiVm+66SZZs2ZNdLi46mItZqE+bsYW/JBCyJlWCcshAsCzQn5cLMw2DZItt2tcn4kbxopKDl2IoJzXt/VYAwixJkPKRbvkLiYGF8BIXDLMyVaS7QtjwPrsXIi/RQCPgHwYko888kgzMXDttdcaa6u9jl0zeYCFG/CL4GaMazeCFfW4444zZFa4Ls+ePduUk6MYkLlixYooGzSs2ljE6Rds0h988IFh3raWV3Oi/qupqTGEVYBfpFIt+1YsszSu55Bg4faMZZ3PCsBv26IeEwt2jFwTYi6bB9jWs+32tHXxQZ+eqZT4maJJE3ra0Pdof72DjujUHjHBnroW8WzVSaot+oDd2iAeXXu2N4mnsTP4FX+ReAcd3qmddCoYMnXsHhnusEO6Bil75CLduJHsCYeIJ68zJ0NKXVafyuxJ6X0/Wn2FGlZIcPkfpV9Zi6aaqVNPp0jIgj3uXIeVSf/LX1DX3UCVtC7+tfOQu71TA6EF98tXp8xRN1OdCEwg6PGMQXdKqOrjBDXSs7jw8CM1m0BnexdAt2FrrtSsLzAL2/HAr/gypYg2XDEaKDrqWPFkqrdWCpI1ZKj4ilzvjhRU9plXdQHwHlYx1li7ADxTZYEuKYnM/ALYEIAfaZBw1+UYMcBYSrH+EpcLCLag1A4FQAvAxvXZukJjXSa3L/uxQl2bBgmwjiswAJK4YdyKraTSFyysgHrnAjCEfOqdd94xsbG0z/WItcVFnFy9CBbpV1991bgPQ3JFqqdx4yIvp1hXbeoiXJ+xuFtGaWd6JNrBKsxYEICsk1Ea4I0unKmacIeGrRlAW1FRYXRoTt75z6ZnYhxOFmgItGjLMnjjQs1y0UUXRVmgf/CDHxgGaj43yzztbLsnbZefoPdfYcSNfXf7nVtZkfYWYG+lTix1kb4Iw2+stbezvtVlbeBhnYvTqITURdlFeZ9qxFiRR6dxCiSUlzXuYGZDP5UePbn5kjP1hE/VRm85ObjiTvXc0IkslWt+tkKfqZ6EIPj6qz6RUcPVqybcKqEtL0i4WcGwK1ENhFvqpO31a+TMw9bI0ftHJtKjB80GrLth+cO3ZsvIftsl+MpPOh5O873i6UeLN3v3Gca96rFVND3iwZbmqjTD73v62eJVr76kRd+/+194cdLV3Yp7RwMuAN4Leo7HAp3ostY91loeAbcAWlL/VFdXmxjY8847zwBfXKoBjrFpkAC7ThboZNMgWSIsQCeuv6xharaSSl8At84USGzTX1yCcQc+6KCDbLNmDWC2TNa4e+PafMcddxgAjZWXnMYIxwCnCERUWNCJqaafEFkhuGkDRqnHmgkJgKdTiO1FbLokxk6sMZZchMkC9O4U3KuR2HL7mdm69rhdUw5Atouz3J7Tk9alRxwq2YMG7HaXM9SqP+Ly7+/2+b3lRO/Q48RTOHz3h+PLFe/+l6i75Ke02u1+D7rFmQDXvPJP5zJaOLhMRh0/uVuM5/PqhFfBa8FpF2pO6RRe7Jyd1d9L/0jNv7rvp/cQcTbbU7dDVa9o1yOupbk5IZn51Adyulp5S4pbJTenTXKy22S/fevkj79dLEccEnnuRMaqTNHb3uypw/5M+h1a8aIy5Ud4JK48e4Fc+7UPZdzQainMbZGS/IAcum+V3PX9d2TK6G3m+uFNcyTc4E4i2A8ju3KEFE6ZplZLDa9JVfTdqXCqPvMrKlM9s9fW95f3k8E/v1rD+ju+V8YbsEeNNuVfO1eKDpse77Bb9jlqYNef3ufYud5y6Xgs0MSM3nvvvWaIACPYniFSYk3MKe7IzjRIxJ3GskA/9dRThliLuGHAY1cs0LhCz5w50wDQeADMgl/LAg0w/Pa3v23clIm1HT9+fDQlU6p9cX6OuAZjyY4lvsKqCnkUIBMCKdyYcTEmTRJu2KQnQjgGwzXCmEiphJ5gaGYM6I9twCxAGyEWmDhmADUTCZyDOzhkWDa+l8+CvgGUSTWFlZrPCGsz6ZO4lmWBJoaZOGQIubCKk8IKK7oV+oFlGxd4+o9L+V//+ldjVQcsx7JAc38w2YBY0i7bVndcc//s++urZe65F4uTETrZvmJBLtkDbtTJXq8718uYfpsE/3Nyh1jgpPpLforMfMnY/9KkqvfmStyPM275rjzy1eskUBuxuKU63hk3X5LqKb2yfsFp35KmN56W1rUacqK/fymJPsf6fO+6lE7prZXDbc1q/W1ny2ecPiVj+8F31pqlsdGr/BTKkB+PoE3PCzWuUsosV6wGQps/0N/IncG/Wnj8ARvNYo93WmdkSrhqvrr0u1ZLq5vBP7tKGj9eqLHA621Rcmv1MhwC2HOlgwZK1A3a94fbZfmP1LLL+5/qKVa8OtnfZ8apMvC7ESLV2OPu/uergfa39s+3Hz3+6tYKaON6Wd92223Gkol10skCDbAC9N5zzz1mARzh/kvMLuRTnAdABGgC6IgNJtYXUIh77uLFiwUWaAivKtTKiUCGhdAPa2kk5pV2cIXmmsQjs89xK7YvsSzQADiIuKiPNfa5555LuS/2Gs41AM+6JcMCzYLYfMAwJCOMlRdbjhNHbJmuiVW2LsSkgiL2F9flyy67zNTBfZqx4KJsZerUqYbJ+bHHHjPHINyiDfTD+BHGh+BSTowxDM7oCYs12xCHWXAK4zTWZ4A4sdJYr2nPMkpTD6s/nzlpkM444wzzueEOj9gxmh39x0SGjRun/z1B8keNkAl335ZSV0lZUThxvIy59n9TOq83V/aWjpOMkx5LaYi4UooSaGV+9UOd0c9N6dzeWnnwQaNlxh++m/LwvJp+6iuPXiUDJo5M+dzeeII3K1vKfvOwAb9hJlmSEJ4mWI37//kl8fUfmsQZaVAlqM81b+IYwdzcUHzwa1XTWmO33DUaaHZayJNQieajDgdqk6iYPlV8BYUy+v5HzIAdr4AJFWBejfKLZPyLb0pGXn7Ceul8oOCgKbLvI/+RouNPkdpWn6aJ80iTMpI36tI2+gAZcfPtMvhHP01nFXXrsSf3hOvWQ+genQMoIsSk2uXRRx81sazZ2dkdWKCd1kJn72lj3rx50Vy1Ng0SdZws0FhmiXfFWgkwZIFVGaENQDCAmvhWWKBx96UvuEKzOC3A9AUQaFmgifnlXGJwIXiiLViKIcKyIDSZvuBKDAmVc8FSSwwvaxiSaRPLNcRWixYtMv3neoBAxkPaIwvWrX7pv1Ow4AI2nTqNJZmiDUjA6LcV+keuYEuohZUYEA7zNeMk7hjB8s0+lmfLAk0fbSon+opFmGtQjth6fO62X0xWMG7E1jM7+g/wy+fO0t1zANs+sy4cN0bKfvwzaQ75lKRYQVkCUV4nc7wmZ4BMuv/OBLXStzij/8ESOoEJpmz1Duja7tOseRyrqssl86xZLviNuWXGfGGaHPWT05Q4m3CDmIMxuxyn3hGXHCvDj5wYczS9dzMKimXQYwskVD5SWuOwwTq106oveq2ZRVL++8clc/Bw56H03vYrY67G8+6WeDLEk737ISa7dc1ufpKnsEInFFJwWESH+a4OYz/WjPwCKb70Wmlqy5E2fWbzbI4VyjjWHMqV4gt/LJzjSmINZA0eIpVX/VJmTbpYfv7+GPnN4nFy5dyxMkkNX3nj05voM7HWuseRFH5RukeHu3svADNWAJYblfkYRmZYoIlRJTYWARgBMBGAEYAMF2Wsk7AGf+UrX4kyEFuSq1gWaM61LNAANKdwTeJmreszANDpIu2sCyDGsgoRFmASAi4sm/Qf9+MTTmgnNUm2L4wBK7hTSEWEq/PChQtN27BA4w5tLeDUBbxicSXeGWBJ/3HBfvzxx02/sO5CKIX1F7GM0rghM8b33ntPrr32WmN9xY0c4TPAgosllrRKCNZ2+oPrMa7PuEefeOKJhnAMIHrrrbeaeljgIe4ijpi+I8RcQ5YFgGfiAAs5n58F3s4x0q94YzQN9YJ/w44/RJ786f3SV/P99s9pkFxfUGEFMc9KxKakJOQD3h7I1hzBBbLP5AM7TL70guHvsSH4+42Xx+4eLwNG18jBU9dpvHxksgY9elSPXk0ztXlzgbwxs1IGDR0pQ3Mi9+Ie60AvaKh16xYpeP6Pqq/BElS9ZcR5ubPDZKrKrylTSmY9IMHtXxZfH1efVjesvdm5UnblbfLcKRdKZd9qKc5t0u+1x3y3zXHdamjxy4qqPjLx+18S/7DI77GzjXTe9hgANkbCNXNTV0NGnnj7TEn9vF58hrfiKGl7X9+tAklagvWdxtN/Ui/WyO4PbcAJR8rHN/9V780aZdRu0dTzrfp8Cem3G+Cr1suWTGnS73Y4O18OPP6o3b9Qmp152WUjZGLRQqmvDcgJ505XLxp1iVZXfFe6rwZcALyHPxuAUqwAiABuuDRjaUQAnRag2fpYfOfOnWvAHyAUIiwn8zLWylGjRhmgBfDFNRg3akApTMhWaBuAjeszYBJX33POOSclFmhS+ACGE7FA76ovuAU7GZbpG3ogPpbFMiRTDqBlDEwE4ErMmK219MknnxQWKxacYqG1jNJMLjgZpQGiWLHRLxZarLrImWeeaVInsY2rMy7RuJQDgLkersgsTgEgoz/AOmmXEMZhXaxxWYcFmomPRCzQnBM7Rsp6g8z//b1Skh2UTU35ZvEqqPDrwzRD14G2DAUiWDRBIh4JzHlfGtZvkbxB5b1h6Ht0DDUP3SLThm2WB98cK2+9OVy/B03qZRCQ3LwWqavNkm3bcyWg1l/0ePTA2dI8713J3n/qHu1DT29s6z/+pqzZbTKx32aZtWGQoR/ipQ7YxtpMzJg9RqrEgv03KNtuq2x79O/S7+LLKHRlpwbCrQFp++vXZcTwWnljzj5mMitXX5azM4PSorlXm9XdLxDMlDEVG6V48Z+lrerLklFW6erPoYGMIV/VMGCNpW6LhPk4DnW9mVkonqL9u66TZkcBs56+YyW8YZZ+kSOeVglVoJMPGZOUHDAjsQt6wnPT4EBWaYnkHjBJal57Xeqac8wSO2yswH3UgJLVpyj2kLsfRwNND10qwQ+fkqm43WfpV/6fT0j9w9+V/JvWanjIp8tQEOdybtEe0oDrAr2HFNlVMwBJZMOGDV1VM8ese2xlZeRlAmAIoO0tLNCMBbFrtnEftgvlgGMEKyu5c1kswAfYWytusozSDz/8sHEXB8CiX1yhcb1mXVFRIZMnT45eh7hnXJixCCNjx441xwDYLgu0UUn0X0ttvax/8R0p9ilxWWazlkcsvs1tPmkI+g34BXxkKhgenlerMVkBWf7oc9Hz3Y2IBsJtQWl48Z/SJ6tevjnpI1NYV+tXL4ViWbK4XH83itSb0iNleY1y8dT3xdtcJ7VP/MVVX4wGal/We0u/08OKamVC+WY9GoG+IdF4LF1YA4V9OkFzoILfIYV1ek82S81Lz8S05O4GF81UFt1qqRy0TU48dL5kqmdHi05oVTdma4ybz+hxwj5rZMr+K9Uq1ygtr9/nKi1GAxkDv6iuzEz2RZ55MYcT7maO/rk+H7sOhUh4ci894NF49Mwv3m/Ab2Q6K8FAM7LEUzpafNN+lqCCW9zS0CTz31gpW5uzjQu0M1yE7TZdqtVra96raohoDLgK24UGAs/dJMF3H9Q4dQW/VoKqN71nm+76mnm3tcXuuntpwLUA74XPw2WBbldyMgzJuAwjWGUtYRY5fRHcsnE3RpJllMZiC0EVqaFgf8YqbPMdQ2RFLDXXoQ4Wc4C3zTtM7LHtw2fFAk279Amxlm+z083/bZ27WDxKIoSUZqkrla9NqtV1qjkE0ZqynqrrbkGmWvUzW9SCpJ9da1A2vv6+7H/ZOd18ZHu3e60rNf59J+FQcU5ALjxojny8pa+sri5WK5tPCrICsk/f7TJSlwx1hUZaFs3Zu53s5lcLVm+XUHNTtJejS7dLkert461lsqNZX4q5/9QtuiS7ScaVVUk/nUyw0lavOUbraiVDSWJciWgguOg1kcbI72x5n3o54/gPpGp7gTQ0ZYlfrcDlfWoly7/TEtfWKsGFL4ucfq2rPocGPF7V1eR7JfDa4WbCoN2B3FHJuenNloxh50lG/xnOUnd7pwY8ef3E/8PNsuHWg8TXuF4KNY2Uzocb0UeLTszos6jiGOn7tYdcnXWhgfXvLdGjhCblSENrphSoZ0dOhipQhcmtula/BJTXQ6MgZN3sRVLpciQY3ST61/rmPfEPKRFb29qPJLx9jU7KDItfxy39XDXgAuA9pH5ruXXGveJmiysxJEqJWKBjL49L7W9/+1tDZoWbs2WBBpiddtppxmWXa8ECTdwubtAILNDE+HLMWlNx3eU4rtDEG8MCTWyx7SvnASipH8sCjQsz1lBAIzGuEGGl2hfajxXLkIy7MAzJCFZYGJJxVYYhGWsrJFOkK8ItHMDrBIYWLFpGafSMdb1GY1oYj5NR2rotcx1ij61wDm1aXaADcv8SB40bezxrfSwL9AMPPGDc0Y844ghjUY5lgXaOkXadY3T261e/+pUhHaNvWMDJa9wTJLC9RsLBdne0nAzNbamxwF0J57jSUQNtNdVaEAG2HMn1B2Xy4E1m6VizfS/c4s7Mt2tDQxq2b9XJmI7xVv3zG4QFdz7c8bP0/mQiJlY8OgEGgHYBcLtmQtUdvZWYeOnf12HhaK9qtsL122NK3F004MkZLFnHzpO6l2dIsGGDxva3/15aDakDiLqV+yV//x9J5vDv2GJ3HUcDoXUfyB+eyJeN1RqeNbZGKkoDhnl33rpceX1JkXxrRo188TTNJJHvhtnEUZ8pathSrZOFkQlAgG4ktKZzbeo0VrnP686a6VgSbo4YLzqW7tzL8GlO7zXidQFwXPV83oUuAN5Dn4BlKYYBOlaISQVA5uZGUpZYZuDYerRBjCtgF/BpWaABhk7mZc67+eabO7BA27ZoA1BHDCxxqrBA//73vzcs0IBfWJSd4I6+ABotCzTpfTgXIM51acuyQEP6lGxfAK2wOzsF5mrciwGfjBGiK8A1kwQ2Hy/Xw9oLOIYJmj5bgVW5tLTUHKeMtmiDcdKGFQi4EAtuYZwmzdHMmTNNOf+sHv7xj38Yiy/xwOT85RrW3Zp6XM+KZXemXQB6U1OTsdzGY4HmmGWBRr+rVq3qMEbbJuvLL79cLrjgAlP0wx/+0HmoW28TH+TR+y0V8Re5VrZYfXkLi7UoDjKLrejY92T6HXvupq9PqU7GxGfdBfTmqHdCIgnrRJivpE+iw2lZ7i3ql9K4PTlurGAihXl8BdI48iF59LLvyBHT18qw4Q36bPKoF1JYGhsy5IPZJbIxcKp8+zQX/CbSIeXBxc9K6yPfkAHZJfLvFf3ltaWx91xYyprmSfNNIyXrsoXiLR7SVXNpeyxry0viDROy1HHCMFYh3nCT+Ff9Q4unxx5y9x0a8PjzJNzS7lHkOKQ3rXq/FUe4YzqUuzvdQgMuAN7DH4PLAr3dpAzqigWatExYUwHBH374oQwaNMhYl/koLJMywBxrKMCY7bKyMpkzZ46xEAPOEQAmbtCwRF911VXGcoy7uWXXtm0B+AG/5513npDDd+nSpcbKTrts0waWe4AqbNrnn3++cZHGEo8VHnA8Y8aMz4wFGmIzKxZk2/3uvC7Zb6QEahuShm5Y4vJGDuvOQ/pc+uav3FfadMIkFQjs6T/8c+lrd72or6RUWgJtsjsPNIjaXOtvx082qOm41IPPeuZ3PBizR7hDsCW1ibCYJnr9bnnlQNlYO02u+SkTLSHJL2jVzATqedAa8eG98a0zer0OPs0A21a/La3/ONs0cfbEbfKH1wAVeuPt/NUkrr+f6vSAQRFrXOCe4yT7krfEk9s+gf1prt9bzg2+/2fpt/1P6vV3+C6HRAaCfsH/SHDOJPFNumiX9dO1gm/KV6T11T/r1zriRt6uB32i+/xK3lbRXuRuXLTRSQAAQABJREFUdSsN7Iyg6FZ96tGdwX3WLgceeKBxe73iiisM8zEs0FYAd7AUs1Afay+ph2z8KyzQiJMFmmMHH3ywHH300cb6e+GFFxprqk1N5GwbcIfrM669uEJjGcbFl/1YoS4uuoiNjcXKiStyIhboXfUFF+r/+7//67AQi2vjcb/xjW8YFmeswggWXKyqsEBjIb7vvvtk+vTp8jfNpQbTNWD0mGOOMXrEZRrB5RnByo0OsdZiMcbdHCF2F6ANGCe/LlZW+j1lyhSTXgnAywIA5pp2G4s510MoY1Jj8+bNUWuwZYHOyckxlnJYoNFrIhZoxgULNPWJN2aMvUFWvrlQmsNKiNP5lko4vMXvrIh7DyY8IQ0ONG/ZLtU1SkiigCMZCWmOxvXLYh+2yZzZe+vUbNgma2vyTP7KVEZJ/uoVVdlSvyUS75rKub21bqihVuqUGIwX4GSlafVWafkk8sxK9px0qscz9ppnfqLfcaVwUrfI2hq/bmucZX6WXPXUj2XfQ/ZJJ3WkNFaera3/afeMKs1rk7mXzzOA15+hIU9ZQTllbLW8dMmi9nYbtknrzBva990tJfxTroO3b1APOmXKP2CNZHThFYO6DjhwtWR59Zw3rzfnuiqMr4GsL1wjnsERo4yt0RJS63p2oeT/cr5OIrqTg1Yv3W3tAuC98Im4LNBHGYCPCzhxyrApA1IBt+RG5gGHpRVwz8QAYB0wa2N57UdkJwUOPfRQU4RVFsEN2gqAFWBMO2zjiswkAvG5lsWZulh+kfHjxxsWaFi2uSZpkCzzNC8t9A8BONvzadsp1tXaltnjdk05Y7SLs9ye0xPX7972hGxVK0YyuA3r7w4lyKrb1iDrZi3uicP9zPq8+h9PyuatBRLUONVdTSYAkmsbsmXHljbZNvvDz6xPPa3hxc9/IIurywRAm4oA8pbuKJMlL8xJ5bReXbfpLQW/6hbeVJ29y/uR+7UZMNfQLHX/+Wuv1sunHVx2bpYMGDNAmFfgNzOoX+aG+oAU9+8dE6KfVj+Jzg+tmy3heljd2yU/KyRvXvqxLPzZPPng8gVy0xfXKrt7+3Fp0zReHz2sGZPih0U4aqbNZmj58xoX1mLGO+WQFbLPPptM/t9YBfizWmX8hDWaj35l5JCeE1rxQmw1d99qQN8Hv3bLPvLbFyfIS4sHyJvLyuXuN0bIqbccKJs3Ndha7robasD5k9ENu9c7utQVC/S9995rgOCtt95qcgRDNIXbL5ZhSJpeeOEF4+KL9RjLISCxsrJSRo8ebcDaSSedJJs2bTIkTE5twW6Ma/Bdd91lmI9PPfVUA/RiwZo9B3ZmiLBwScZyizWafpD/FtfjT9MXew3WuC/Td0ArhFdcl3hhgOr3vvc9UxWAeNxxxxkSLHRHyiIIvNauXSuHH364yU9MRUilSFNEPDRu1Vhpr7vuOgNUKcfairUWPTBuLOb/+c9/jGUcfWKRJd6XerhLA3Apw3LMQj/oF0J/Y1mgAdX07e9//7tx0zYV9Z+T6dr264YbbjB9oB8WSNv6PXEdqG+S7cs3aMoEr6xvjMS2A3LjCWkV6pRtslaJXgJ1jbLqzfnxqqVt2eZX3iJUSJat6a/3HF4H8VVB3CAsvGs3lUprXYNsffuD+BXTsHSpAtiaOo/M3jjQkF4lowLu11fWDJPaurAsfXluMqekRZ2mWS9qfuRGZW3PkMatuQYE4w7tFIAvZQFN1xVsisSjBz56y1nF3Y7RwKO/+Y+sX7JJWluUqFKP2cmuX5xwg7Q0R4BJzCnurmogtOI1vdFSzKWM5tTyFlr3vqvDnRoILVNulJZ2PR534sdy8hc+kmEVW9Ulv0nfgZrN9vEnLpCjjoEpeqfoOaFlz9o9dx2jgeceeEO2bdohryzuJze+OF7+79mJ8uQ8TZupVuA/X0EMtSvdVQO7EzLVXcfSLfoFG7MVwNPKlSsFEOdkgeY41kBce2NlVyzQgDUkEQu0bQ/wlogFGsuoU+hLPBboa665Rr71rW8ZQAx4jGWBTrYvzmuxjXvxtddeG2WBxu0a0E2crxWsu6QsuuWWW6LxwRy/8sorbRWzxq3b2RZAFsst47Fy/fXXm/358+fLTTfdZIpxV0b/lpiMCQdA8UsvvWTifamEXnCPRnClBtgixBFDFgYLNO7NgHKAcFcs0ADyRCzQfE5vvRV5eWRSAHft7i4N6jKa4dd7USc4ya+6piFPijTdUZ7mC/Vq+iNe8LDFtYS8skPTKpAbGAm3aRqrNVvMtvsvooGW7RH32zbV1cLlg6SsuFZKijS2WvWIuYh1q1qHt1YXSHVtnp4UQcmN6za4KtypgbrNES+Q9fWF8srqYXLU0DV6JKxpozqrqE2txEHV9SwFy9XNOaZC7YbtnSumaUnbtk3RkYeCXqnfnCeZOUoUlh1Udz48WTzS1pwhrc2Z+n1uV3BY01CFdYIvVWK86MV6+caHryw0z5R4w1z/yWapHO+SNsXTTbh6FQ+OeIe6LiMms8F91lglhevW283oeviIrcKyKwnXRd6DdlUvHY+/9+J8Cel7TayEdOb/w9ddb7dYvXSn/chbaXfqUQ/tC1ZZwAvxo1Zw5QW0nXjiiQZIWrBFmhuA1ZlnnmmrGnddLIwAsEQs0Mcff7wsXrzYWBKJGb7ttttMiiXSLFmpqKgwlkpA7oABAzqxQBOb+/777bOi9IU2Y1mgJ0+ebCzGxL8CpokRdrJA76oviVigAbtYZRkjgBoAy7Xpl1Nggv7Rj35k4nax/DKRAOAk3vaOO+4wLM7Ut23BxIz1Fws2hFiWVZo6d999tyxZskROP/10If0QfcASDDs04+MzOuOMM0ysMOAcF2gswNTDNXrWrFmGedpOHEybNs3E9AJ6sWbTByzs1rruZLqGgIvPFSs07SK2ntnRf1OnTo2C/wcffNAWd+u1P09jVh0pkEIKyqpbs8ziVeABCG7TF2WNeOs0Dn9+dqeydC7w+jOjww8pMNu8vdgsPp1MyFByF1yj23SJFV9exPIeW56O+5k5ESskY9/alCfPrBgh+5ZulSEFdWYCgRzA3JOs19YVyKKtfaVRPRKs+NU91ZWIBjxZkUmBqD5UZ62NfrNEy+JuEN/a+T6NWzUNCwv7FsQddXNDQPKL3e9yXOVQmBVfbwnrRw/osycz5l6OHkvDjU+jC597fya6Y4rKCnViUN914rjA5RW6918ivXWHchcA76FPAYAWKwAd3JPJPwvIsyl1AL/ksiUG1ilYJSdMmGCAGGROgGrEklxhjY0VYmdJNwR7MUK6Hdx1bRokgBkMyIBA4m+Jh21sbKdspy8AXHIKA3KJVyYVE8AUEixSA2FNxYKJJNuXRCzQtGmF8QJMAcuJhDjgcePGyb333ht1QX7ttdfk7LPPjp5CzC+WYdoiVhf3aStY4QG7gFSstgjjRc/o4tVXXzVplADc6ACyKtyWrViiLSy9luSKSYrf/e53xuWaekwWAOYt6zTXIl4ZgM1kA/cBrtXoH7H1zI7+I7+zFfraEyS3byGKjNtVwDBAI574crKk76jB8Q6lbVnesMHSvKmq0/iDQWWJ7VQaKfBmZ0nBPsMTHE2/4j7D+smaWe1uew3qdfD+poG6hDUFUlCydWlWfTbpojduJwX1qezfqSxdC3xDRknL4g9SHn5GqavDrpR2woVHyusPv9uhCi/OmepJUzqopEO5u9OuAW/ZGGnLVADW2v7e0n60iy39mnuKK7qokF6HPKWjJbzqVbWmd7ZWdqkJj2bk6DumyyrpfHD6aZPl0VuUNyGOEo49a1qcUreou2ig3X+pu/Soh/eDdDp2Ia6X2FUAHlZJXHqt4KZr62GFxAUYdmJL9GTXlgWamF4YmYkDBmABumA/hr3YglLbNmtif7GcWlfoZFmgAXo//elPpa6uzgB38hFj+UVS6UsiFmjTkP7DpRjXZKzmscLkAMDU5it+/PHHozHOWFcB5VZwO0YPK1ZE2IUBmk7Buk38MhMRuBoDzLEUQ4JFW1bPnEPeYSYPALfUIycv55NuCbdkO4FBXDa5h1mTcmn58uXmc7CM1nw+WKRZiKGmLVy6X375ZeNy3RtYoL1q6ak4bHw8LOFUf6ftjEyvDD9qYqfydC4YOONoydCJgVTE61PvksMOSuWUXl133xkHS3YR7uGxonH8wUzj6sw63g2bU5wnY06cHHti2u7nHnKiePKLUhu/5qXOOeSk1M5Js9oHHLuffPs3p5tRZ6qhPDPDI4MqSuXhqj934JBIM7XscrjeEUdrPG/qthpPllrmSkfusv10qZAx8mS1pqf4vUY5ek7GSPe7neg+mXj4vvI/N5xhDns9TC7opKvm+D7osOFywdWR73uic93yz1cDLgDew/q3qY1Y4/ZK3GcqaZCslbHSYf0FxJI2CEvyEUccYcAU8ayAKmKLsRpa91yGQ30ANmmQKO/fv78hmAI4QhTVlRDnihsubSCW+IltgHayfcHSi5u2c7Eu4FhFIZ8i7tWW0b6VlpYWufrqq+XOO++U1atXy+23326swBznWEVFhalKX7/97W8bd3IAvgWg5uDOf4wdoT/oC4CPDnE7pi2bMunhhx82rs4XX3yxmYigHtZzLMNYlRk32/SXNQAYsE3MNsRkCPmMEc5F76Sr4rrsf//73zefCdtWt6ZyD/53yKUzxJ+ZyEbZeWAefThU7FcoJRWupcipnYGnHCueUCpspWEpGtpXsBy7EtHA6OMnye66MWcV5MrIoye4qtypgawJh4qv78CU9OHNzpX8k89J6Zx0q7x54SpZcPODMrm0WUblB2TfwmYZXLtO7j35CjXKpWiVSyPleUuGKQA7VpR0IvlR+7IkY/rP1TXVfcW1SvMMPUI8hanHmXuKholnyGG2GXcdo4G2uhrZ9+mr5BeTlsmpw7bIiYOr5KLRq+S7GU9I7RsvxdR2d7uTBtxfh73wacRLgwRAeuSRR6ILTMK48WIZxe03lgUaa+7GjRuFFEDkw4VJ+Pnnn5d4LNCAQYAcIPmxxx4zI9wVCzSVAJS4O+MCjEU6EQt0Kn2Jp95nn33WxM7iAh6b6oj6uHMTY4wV+LLLLjNjti7erLGGI7hFYwHG4ko8NYAU67HVK+MhDhrLOX3Gkky+YVyoid9FiAtGmBhg0uC9994z8cZYu/k8uJ51VcfNGfdx2gWcWxbot99+27RhJwsoB+QyYWBZoLEu0z5t9BYpy14nkyds0jjVZAhKIM/xyjFTF/WW4e+5cbQ0ycBy3PviOVHFXoY6HikrdEmbnJrJyPTJydefJ1n5HT1AnHUSbc/4zfmaE9ONXbX6IU9tn5/9SQFH8la30l/cIxml3Z+8z45xb6+DgVa566j/MZdVJxgp8mveeJ/GTOs889Zl62XB42/u7S71qOtlnnKzSF4Zs/u77rcCZe9+p0vmpG/sum4a1eCdxDfjTglnpBbP6zvl7l4zaf9ZfNwbfvUTc1+OKGiULyoAPnvEJplYGmHb3vz7ayWkhhZXuqcGkn/Cdc/+94hexUuDBMMwRFBWAG+45AKSLr/8cpMLl/heANMJJ5xgCJRsGiTYlyFfwvJLep377rtPYJ+2Maa0CUgE6OEKDWkTlkjcsYktdlqL7fUt+IVECssxLswA91gW6N3pi72GXUNuhSW0KyF+FrdmgOgrr7wSrUqcrnUhfu6550y5dTW2laxeiQXGfRmXboA2ruacbwU2Z0jAEFyxR40aZUDtOedELBmcc95550Vjfy3AZdIhlgUad2hisRHiqrEs4zZ9ySWXmDJiqJm4YNKCyQusyFaY/Fi0KAIMiTnuCSzQ9D20dYUcOHqF+EIN8u5HI6QlCIjo/IKSqfGXhXnN8uXj5khGbdefu9VJOq1b16+R7DyPDOu3XdZsjsTyxyMPw70q09cmA/vukLaqyAM2nfS0q7GO+8JUWTFznsy+L/lZ92nfOsF1f46j2MyBlZI1YZoE5ryhRyOTLp2r7ZywySuQrDGTOh92S6Ia2Pz2s/odD0tzQ+ffx2BTi8y79yHZ70tTlESs/bkQPdndEE9uH8m+9H1p/vUAEZ9OcgWb4mslu1C8ldPFf+rt8Y+neam3fD+p6vdLKd/wE30PVM/yBCYwjgVbfFIz4rcy2I3/7fKuaVowV38iE0xea3lg2SLJGet6GHWpxM/poAuA95DiK9VlGbBKXK8V4nOxOMLS7EyDZJmXbT3WkDWxnHvuucZ1ljLchzmXeFziUiFLgmwLUiWbBgnLZTwWaAB2bBok3IHjsUATHxsvDRKMybEs0Kn0hTHEEyf4hXgrlhWZcyC0Yqzk+MXqCjHXo48+aoC8bdOZcop+YbXG0os7s1OYXMCSTNw07ugIFvaLLrooWg0dAEyx3kK6NWbMGGOBZvy4PcPkjXUewbWZyQQnCzRM0ZyLUA/LMUDcyQKNRRgADNhGt1aIDce9HeGe6TESUsuvEmpMGL1e+pXWyuz5lbKxqlhni7FsRBh3c7JbZeLotTJ2xEbxZehTlXNc6aCBcFvE/RlwW9F/q+yoV0+GpmzD/oweebT69VhRXqMU5AaSMoJ0uEAa7Yw+7v+z9yZwcpTV+v/p6W32JZPJnjCThCQEIkuAsAWCYRcFWRRB+SGIqIDK8pcrVy7o9XLdxcsVvaIIXGUVEAFBvEBYAwlJgISsZN9IJsnsSy/T/T/ft+edqenpnumZBDIzXeczNdVV9b5vve/p6qp63nPOcw6XpY/M03updSnvDjispb2gICCUdyWNBng71usv4ZiQ5gVP1ZujE7KupNdA24o/Svj1n6sqJ2qh1G680V1rJXLfePFfukk8vtz0jWXxEU+gQHJv3ibRt36ry/9Ifc0eiSq/B3OuRYX5EigdK745/yK+T1yYxVrqfeiRnDHy8hNHyEFHrpWyEY0mtZnXp89mlbYIORw0o8OHhbJi0WSZ/E2dcHClRw30FL7Aezip4VwZmBpwn1z76HsBrEG29MADD3RrEdbfq666qiPeFQthKsF1GWsuoBnyKSuwDhOvCoC7/PLLzW6AIZZgQODhh3e+xG3Y0JUFGoIoUjMBHrH+YlFNZoHGbTg5DRKxrU888YRJK+Rkgc60Lz2lQbLjAvgCbJNZoOkPRFWMCwu3M26ZvjvdprH+4l5s2ZZx88FiDEi1wsQChGSWLIz0RnxfF198sXFlZmKAWGOrF8CoBaS0QWokrOFY6BHyBSezQGNVt0CecpwzmQUaCzBiy5kN/efMbXzSSSfZ3QN+7SnRh2OuWnRb6mTU8Ab5zMnv6dg80tica6zBecGwFOZ3df/xFI0Y8OP6uDvoG66uo+3MnF5vXMpLms3CpDK5gUmFlOz55y0p+7i7OSjOV1Y5UnwK2vLUWh5RJnJePZzQDTjs1T1+LeNTEqKyKvcFL90XG5xyuITefU0vwvZJOaciqdQ+t+AbNzldE1m/v23zP6XtjRtk+DC96uKTUuojRycGD5isk6d6D4j87RTxf+Z5BcF9c1NN2fAQ3OkJFor/xBvFN/sG8e94X7579dVymL4XfeHr3xZPyTi9T7ZflENw7PtqSEWTJkhLY74sfG6q5BWFpGJsnQTbn9Otmu5s15YSPa4x1LkBKZo4YV+ddsi2E5wwUVpXJwwYyYOM1ddKsOrA5N3u9gDRgAuA9/EXgcXQCqCIuF3ckH/6058aqyKuyAiuzjAIIwBnQOC8efMMeIM1OFUaJFx5rVx//fXmY3IaJHvcuj4np0Gyx51rHhrONEgwI3//+983rtKAM9yerVgQ2VtfGIPTGk59yLXs+LGSAuKxhgL8nYLlFIvuokWLDHEVJFNPPfWUic/FfRrrKy7gCECUCQPYlkn1RAzvbbfdZizuWHkR9Ex/sAJjyV2wYIHJoQx4timVINCCGRqyKktExveHXvh+mMSwABjXc8rBjs3EAYRdfH82vRExyQBo6tMvSLTuuecek5qK/thyfB7M4j1wNibrLkMAwJUUpXZPIwbYP0OZKF3pogHfCAVhGgecLLzLGat58gHd9pdpGipXumng/ddWS0tzRAJqvAxgvVSwy0SCFef7cVNDSFa8vlqOn+iCYKsf5zr32LNkzwP/JT7VpZEU2ALd+o/5jLOa+7ldA/HGLdI27yqzFQy2yQmnrJP/eyrx3LJKghgw1pYjR8/epLtUmfXq4bXyfvEd8jVbxF2n0ICJZx11iPz0sZdTHHV39aSByLoVIqFWLZIjLQ1B2bQyzaS0loluWClyjJu1oSd9ll3xbdl+05UpixRecIV4C92wr5TKGQA77aNtAHRlaHQBq6VdZs6c2WcWaMASYtPz2NRDuD9zDDCIdRPr7xVXXJEyDRIPBwA2sbzWFRqQSW5ftpOFsrgiI1hHS0pKjJUSsAqBlJW+9KWnNEi9sUBjncUaS1+ffvppAzABtgiAE3CPwCI9f/58E2dLX+k78cG4m5MHGcG1+cknnzTjI+YXd+Y5c+YY9miOM4GAQPhFXC6kWLBFs+DSDohFAK02DRIA18kCfckll5i+WuBMPDULLtb0C5dv8gvjis33ZmOYTcOD+J+nsEIaawtNLFFmw4hJON99mCbrKrToRfHn87vs/ttMLmu3PbvXSFvdbrvprlUDkdaw/O2Hj8mukL+LPgC9dnEe2NnqkydufURj3bpO4jjLZPPn9x54VTbsqEj7+26LeWTr7jKZf987LotxigulbZlOcEebO47MPG6rfPqiZVKo7M9BDQ0JBKJq+a2Rb/3bK/pcaP/tt7VIbMmPJN6a4JPoqOx+6KaBhW++Jzdc8x/y3et/IrU19d2Ouzu6ayCq7zTbf/87KcvnuuzpeROX4YWNsv3u30lUjRGupNZAm5Lb3f/Zn8j8TRP0HVDnsaM5ZonopNaa6mHyxO2vyJ41W1JXdvfudw24APhj+ApSsUCnO611j63SmGIEiyuA9rDDDjM5agFol112mQG+gDnAXnIapGQW6EzTIFkiLNyBZ8+ebdyCLQNyX/sC8HOmQOIz/UV6Y4HG+owwTgAwC2mTrFiXb4A0oPKoo46yh8wa8G1ZnsnRi1i9mg39B1hG7H4IxBg/JGAIIPwPf/iDYZiGSRrQS4wwwvfhFNuG3WeP2zX7TSyI3iFZO/fbOoNx3bLifdm9cZhEQ760L8l2XDwctr4/UqqffcHuctftGmh++a/iizeKL6OUUnF1VwvpBJcS6ixJTN64ikxoYNUrK6RNwWyruo1va0n8vmNJ73hst+nC8bCWi4TCsuaNVa4KkzTQpnpZ/eBzsmF7mSxbPzbxUqckd1ENcYjoSx4veNt2l8jKTSOlds0m2bNifVIL7mZswzPKedA1BGT6oTvl6ze9IVfdOF+uveVV+dyX35XcvO4TMPHtr7oKTKOBuvXb5BdX/rucdfKX5f4/PC73/OZhOXDMyfLSXY9KqK4xTS13NxqoeeVliatHW64+a0YWNWgUA+8jTPJzo0x8hmxxeEGT5qlW7wS8E9uNBNR3pasG3n/gefEG/dLYGpQX1kyWRVvGyZKtY+XVdVWyoabczLy+/sN7u1ZytwaMBlwX6I/hq0jFAo1l8d577zVnBxRhbYRIiTXWyOQ0SMSdJrNA4xYMIzFxwwC+nligrSs0oDEVALPg17JAz5o1y1hJceEln/GMGTMEpmM+97UvySrujQW6pqbGVIEsDGs0YlmTsaASI4wQS41VPNmFGrCKizWAlQkCBFIt0iORAxjXagvsrTWW/egd8A1RFmmOcHkm/hhWbsSyQOM6zXEmI7CKw+KMFd0KugRg33HHHSZeuaKiwrhAA5RZklmg6Q9tItQdLNK8crlEmkOy+b3RMnrqTk1BE5YcdYF2zg/E9IU5ppainWvLJdQUlKjWcaWrBsLrEzoJ5kfFo19/RCcUEuKcaNEXFN3MzQuL1x9TC1GTRNa+JzLns10by+KtTe+q50gdlg39HcW8srnZIyX+NsnztkmO6g7w29zmlbqIAjl1x0datPzm9zbKQXMONtvuv4QGalZv7Pgd76wrll3LCqWssFlyAxEFwF5D1BaOtk8itoZk59vLpfzgSa762jUQb1Pg26pxvSmER0VeQXfQ21E0rJ5HtavVQdUVpwYiTS3y6s2/kVX/eFP+c2NiYpvjCR8tkRu/+0u59q4n5LBvnG8WZ133c0IDDfquEVN+EgSAO7qkTkIRnQzUCS0koPuC/oi5X7IdU0+8+sWLZLh6xLnSXQNr/vaaYAVG9M1HGsPBroX03X7r/GUa5qDPIK+36zF3a79rwL5p7feODPYOWCugM+41HQs04BfQi4UxWSCfgnQJUGfTIEGMRFxsKhZoWKMRmwaJflhrYzILtE2DZPtKPduXVCzQEHGRT5gYV0i8AOd96QvtpxLLAk2cL/G1yWLZlgG/MCsDQm05CLCcbMsWINMWC2JJsogvxhJtY26JBbYpkgClCORiCJMCEGXhJk58MzrEusz40Tti+4UrtU2DRNuAZyYgnP3C6o9LtE2DhIv52WefbYjFklmgiV0m5zGSanLCHBiA/5a9tESKNQaYPm9fNUL5sEJSWN4kuQqEPWqhjIa90lSjjMa7CvQBkLj5N1fvkaaGFiko0lQWrhgNxJo63fcCuVHxqWskukNnehmqfuMG9PoUzDknF9pqEpMmrhoTGlh+z5NdVBFRkLsrzItdAqh1Odi+gX5X/fkZOe2bbmy6Uz+hXTskHq7TXYkX45hay3fXFzqLdHzGWhzauaFj2/2gGmjV8ARSGrX1c0Kz+UNXjQ4NRDW84bGzrpPGLTuloblVubQ9EjZWy85CNfouEaptkAU/ul+2zV8qZ/3vbZ0H3U9GA+H2TBVWHUwM5umkVk9P4/DOalvcXSdpoLUmM/fwqBoKAkUJD8ikJtzN/agBFwDvI+VbK2QmLNBOa6Hz9LQBKzIgDfBp0yDBxpyOBZoUSM40SLQBwCUGljjVZBZo3KFJzWOFvgCC07FA0xbgF8Inlkz70hMLdG/MzZZs6s9KmmVdkgFZuGUvW7asw205HduyzalL37EcE4sL4ZZTyNmLpdhaljmGhZc0SoBfBNDtnCyw/WIfIB6mZ6zRjIc6tqztFzmIrX5h58bCjNhyZkP/wWJtgbgF2/bYQF0vfGGpPP3IYrngAI/kqtUX82RrQ65ZeuozFrjvnvcL+dXzNw8qsN/TmPb2WH2TpvFwNIKFKJDbpntYUguX6PbdOaJOVq6oBlbcqV4YDTV6FeabmfhMlYILYGzHdln9u4dkylcvyrTakC4Xj9SLb/mX1KQxWceZAMA9DdgXjElg9x/VavlJySl1LelGV3nqeRTrPrnbkx47jnlU54XjOzaz/QPg9w+Tz9dLMeHGUaA3yHxdwo6UekyvjlDvMIS0NNveeE+txXfJ7Nu/Yfa5//RpohNV219dIvmqLOdEak+64Tmz+z3lm9C63mAiC0ZP5bPtWPH4CtmzenOPw/Yqi6ALfntU0X476ALgfax6lwV6j8mB2xMLdG/MzQBTBCIwrNhYWJcsWWKss4BOa9GlXCZsy8uXLzdA1BJl4VrNJABu0XbiAtdjGK+JU8Z9mXNiScY6S3mIx2y/+sICjXt0byzQ1mLNmG1/+DxQBbD/06v/KK31+epWqi8lSTPx6fqNC+qqunx5X93G33r+PTnmdDc5/LJXVsr7mwvl2LG1HW5n6fTn3N+ibrx/fXy7TPxus+QXZ/fMcri2Xj7441+kNCckXk+eujdzTWYmXrWul8SbZJUC4MqLzpZAcWorZ2atDY1S0Xf+VYpHtok3oM6lCY/yHgfGy/SoSbs1N+vXxH/qi+r9kd7i3mNDQ+igJ8crnuKJEt+zrM+jinsLJafiiD7XG6oVFv3qIfHla/iMWtEQn15wXy4ul1/W7pRcnfLir8jjlUuLOqcDcUv94K8vy6SzZ8uY42YMVdX0aVxrfv+wRCSok/WtfQLATXVh2fTYc1J1scv27lR4PBqWSZ77ZFtgmhowUt/ziK+uHPuBxKpX6W96qrO6+3kAaKD36d0B0MnB1AXLAM3aZYH+gfzgB50LADIT5mbr1lxVVWXYmLG2AkjnzJljLK2QUiGAVpae2JaJtyV38fHHH29idUlHRJ9wbUZwU0cAw7gwE+uM5ffzn/+8XK05BgHblinaZYE2qpI3/r5EanbWyU4lftjclGtiKxNHev4PMHlhe7k01bfI7/7tkZ4LZ8nR+256SF5eM1xaNa6yLxJWS/qyrYXy99+80JdqQ7Lspif+qVxDEY331dc7zZncFyE+uFgJYai/+akX+1J1SJaNN26U2PZ/6mRMVGactkP8xhOhp6HGZewhdVIwLCLxULXEtv69p8JZdSw07CwJh/r2u0ZBkVYNdRg5K6t0lW6wIXWPWX7f3zvAry1X7vXJ98pGyTmFpXK+LteWVujkV9eJr3B9s7z1o/tslaxeR5tbZK1OEjY3qccLMwYZChPc1Fn+iz+oN39iAiLDqkO+WOS5m6TqwHoZNVonr/U5kiyAX1I/HnvMSgk/eqnEbT715ILu9n7TgAuAPwbVuyzQJxt3bqyrmTA3EzvLBAKEV07B/RmBoAux8bJ2zT6sk3ZhP+7GY8aM6eLqTDlczRELpm1MMOzPWICnTJliwDWuyTY1lcsCbVQmrzy5SBpq1W9X5X/XjZZQO4FG4mjq/xElwnpRGWU3N+eaAhtWbJP6mgSZWeoaQ39vY02TbFy2WbbUFcryHWUSVR1lKo8vq9QXk6i89shbmVYZsuW2PPOSeTnj/XdGaZ2+3uGSn5kcUpKIv27TuMKt2k62S9uHOgkAgZPKpGP2yMRZGsuaRnwKjguHh+TYi9tdANV1um3zX9OUzr7dq94dI2FNtdUXwat34YsTNCyp78C5L+cZLGW3v6nP/DQ/51x1g54RzJODAnk6YZP63rln1UaNC87u5wzf9Z5F6BFFeqS2WcNE0ug0+brY01xg6lBhN224YjQQb62TtqU6id8WktNOXypTp243ac2CwbC+a4b1HTKq7501cullL6tXn76T1m+X2MbXXO0NMA24APhj+EJ6YoGGCRpGYgiYLr744oxYoLGMTp06VSwLNLlubdofO5zPfvazhrGZnLmbNm0SWKCPPPLIbvGntjyWVIiwiLnFQgobMjGrsEAvXrzYWEVTsUBn0hd7DtaZMDdTDl2sXr1afvvb35qx3XrrrWYchx56aEcsLX22bMuAY8iyfvzjH5sxAnwBrIBu2JpffvlleeGFF4yV97nnnjPj4jyQeiGA5NNPP91YgrE4E1d9yy23mFhfYp+RZBZoLMbovScW6HT9Mg22/2McxBKzEI890GXV4nUdLyW7QgH5/rsTTZfDyvicSpo1bcqrO0rlr5tHdhz2+b2yadX2ju1s/LBl5TYlvEq8IN+7aIrUtASlrZevnzQ0T7x/gLy9VWMMVbavdslyGjd1chqUKaHLkWUJFvmOi7TbxRVXN8qYHDNst5RqeSuNG7baj1m7ju96U2NXO4mbjjhnuxz9+U2SXxqWgLKUB5S92Kz185Tjd8mnbuqaQipem5hYzFoFOga+7qUV8uyfpyuZnWNnDx+jEY8sfWuMrF5WJbuWr++hZPYcql76gYQbEpOt/Rm11+8TQHC2S92qdRJpTMQzhNt8sqspEepBWFKysI8c37saC3RSNjERE21SNmhtw5WEBmKbF2hMeufk1vEnrJbPf+ENmXvqMjl57vtywefelDPOeldBcfuPP1QrbWtfctU3wDTQ+Q0OsI4N1u7AxmyFeFUsigBg0vGQssgKVsr+sEBbq+hZZ50lv/zlLyWZBdq2j/UzHQt0MsiiL6lYoAGdX/nKVwwgvuKKK7qxQGfaF9sn1jApWxdn534bB2sZko855hi58cYb5c477xTIsBBSFtkcwWzTVjLb8vTp07uxLX9KKfyZBCA+25JfMeZTTjnF5FemLeRb3/qWcZemDOAX+fd//3eZODEB8FKxQAOWIefqjQU6Vb/MCfQfaaGcLNDWKm2PD7R1zc6E1cz2q1bTKNz49oFyyujdcvTweinwtZk4TOIrNzTmyXNbh8vKemaSOwX979pugUrn/mz6tHvrHiVsSbyBxNRV6tZ/zpQLZ6yTWeN3mjy/ub5ONNysrNARZeJ9fGmVLGwHv+jKo8QwjWqNLyztqt9s0mNbS1fXvOG5YTm2fLesqC+ShqjPOPzhypej1yPaLvZF5SC1/JYk5V2O7MWL9lDRd7yl+6TUpFk1UnVUjTRUB6Wl3i+BvDYpGdUqXl/i2u0ydsMc3WVP1m40V9dK7fZC+dMvjpbPX7vI/Kb9xFWnkFCLV9YsrZA3np0ouaX6bNtZm6JU9u1q2LRjrwYd16TfTTvSezHsVeODqHLzFp0odUyuRzSEZofeHwuCIckzaY/sc8gjLfo8bwrpZGx7qjiGGdeZ2abN3e8Ng0gF+7Sr8Ua9Lts9ZWzDwSBW3zS/W33fiddusEXd9QDRgAuA99EXgSUU8Pbzn/+8o0UIjXCtPeOMMwyQxBqJQKqEJfTCCy/sKAsoxcJIvGo6FujTTjtNVq5caSycMEQDDpNZoCsrK007tAeQSmaBPuGEE4wbsj0xfaHNZBZorMUsgEbANGl8nCzQvfUlHQs0DMk27pY+kGN33bp1ZmHbyZCM1Zq0UJBhwab8+OOPC0AcqzBs1rYt9AWpFfojT/HTTz9NU6Yt9MD5rrzySlMXKzDpkA488ECTogjrK2zNsDR/85vfFIDqqaeeKmPHjjV6uu2220zdSy65xJyPdo899lhTFtCLJZ7YZvpq+55Jv2jHCpMYWJ+R//zP/7S7B+y6oCRPpN3r0XayUYEGFl6WYI6yb2s+QfYlSLJsqc4111RRWfaCNjRROKyww40/oRmPPLp0kjy/ZpwcNKJWKsvqpUgfqjXNAVm1q1RW7MRNuqvTTjQSlYKSxH2lU7vZ9SlHrTxtLV3HjGX32OF7pEW9D2ojAWWM9UhAU3OVBsKaFzg1CPHmuiynEizrqsj2Lb39SsnIkFlSFrA7fdl9LVo1sA4WJ+5vTfVBeeCXR8mMY7bKtCN2qNeHxgbqJQjZM5My1dsK1e35ANm+MZHzHs+F3FKXjA0d5g3X2YC9EZ0gDJa4ugyWd/9dA3DrW/PM0uktk9qLi68gd3j3NvbmqxnMdT25+ltVoru+iKegoi/F3bIfgwZcALyPlIylN1kARLgnw1QMwLQkSoBf3F1xfXYKsa+4+MKgfNFFFwmgGnnmmWfMGmtsskDQhMVy2LBh5hBArqampiMNEsBs3rx5hs34uOOOkw+UgbdZk5tboS+AEazJgFwsqsTHkg8X1mQYlLEOAwyRTPuSjgUasGhjedEPOYYBrlhpEcvwzLEf/vCHZkKA/fQRgqq1a9cad2ZIqmgLcIylmNhi6pC7l3EitMV4Ac3JAukVrtHEE8NKjZUevXEemzIJXUyePFlIbcX3wfkQJimog8s1wkQBOZJt3zPpl6nY/g9AbeWOO+6wHwfseuSE4bJu2Za0/Qup2xRLzxKXirGJa7bnckP36PBxqcdfp+Rib24aaZbeRl9cXpQEonurMfSOB8pKJFLfmHJgeWpFz/N1uvSmLNS+Mzg89ffRU52hdsxTNEXixAHHM/TbTVKAJ39s0p7s3RwxY5LsePcD1WVcWpv9CnIrzVJQrKE5misdgizAcVQZ3Z0Si7ZJ2SRXj+ikpHJ0FwZop54y+ayPcykalwgXyaT8UC2TP26k+ArzJdruBt19nOmBL2Wpmze2M4Spe/3s2pMzdqZa1PtwjwwWSc6Ezve87NLWwB2tC4D38Xfzm9/8pqNFCJSwTBKHi0stLs+ASgSLJpZIBNZhLKYA1Rdf1JcPFYAdABgAB1gjprehocEAPVyPKysr5eijj5a77rrLgNIvfelLpp79xzkBVuPHjzeu0JdeeqkBtbieJgt9AfwiuPR+5zvfMaAS4H7zzTd3uG73pS9YmpNdeQGGLAB2ACNxzwsWLDDW1urqanNu3JyZHPif//kfE7PrTIMEIKavgHKEtsjBC+glVtmZbghrO21hhaeNhQsXmvaYYMDaSqwzMc2wSyMAaMDvyJEjjdUbq/CDDz4oTz75pLEgM7GAtRyh3jXXXGMs4kxsfO973zNl0DWSSb9MwUH677SLjpN3NH1PS2NmwCLVMAvVajnhwNGpDmXNvrFTRktBab5AhtUfwf356HP0QZzlMua0E+SDex41bnr9VYXH55Wxp5/Q3+pDpp539KkSW68hJ5E0rnw9jdSrec/HJfgSeiqWLccmnXWsrH7qtW4kTIBelnRSduB4yRtWnO5wVu0ff/JMeev2e/s9Zr8Ct5KJ7mTCiBOO6rcObcURJxxpP2b92lM8RnLGHC6xDa9mpgtvQLwHnp5ZWbfUx6aBrv50H9tph+6JDjnkELEL7rhnn322AZG42r722msdAwds2XIwHuPSTHwrVmDEaf2l7Be/+EVjSQawYS1dunSpWRNbTF5aZ1wv5QGKuNOyH3dhUvoAHK3VsqMjSR/oJzG3tIFY4ic+Y/3NtC8AfcbkXAClWGeJjf7CF75gUiIBGgHlWLIB9LSPhZpYaoAkY7VpkACvjMcCTfaz/UnN0cv52AaYMnY+0xaxxViGcX2+7LLLjHv5nDlzZNGiRUIc9Wc+k8htR5/ox+WXX26s4ABhYnNtnDM6DAaDhlSL9ec+9zkDvHHRpt8IbtNIJv0yBQfpvxM+PVMCwf7PneUVBOW8b5w2SEe/b7t97g1nSW5hghm7ry3nqh4//U1Xj+M/M1etROqWvxfiy8uVcZ86eS9aGBpVc4brPbggMZHX5xHl5Iq38uI+VxuqFcafMENK1YLJRFWm4tEJ2+P+5YuZFh/y5bDejj52Rp90aJXiL8iTw66+oONdxu7PxnVuxTAZMfso8Xj7/spPnZEnzVIXaNdDxnnt+M/ScEfiGDIQ/1m/EE/QdcXPQFUfa5HMvr2PtUtD72Sp0iAB3B5++OGOBSbhf/3XfzXEToA2wDGWYcu8vHnzZtm+fbvJZzt37lzDcPyPf/zDWGeTWaCxepLnFpD86KOPGoX2xgJNIcAv7s64KMMEnY4Fui99Sf42AYyQYMHQ/N3vflcq1ZKN9RsACkhHAPUQVMGyjI4YN3G9S5YsMQ8zrOEIxwG5AHPLtvyTn/zEAGAszAjWZqzkuJ/jokx71113nTkfgBUgjNj4bL4HJgoY49133236yXG+L9oEnKMnLNSWBfqNN96gSMdkQSb9MhUG6b8iJVy66NtnSX5BoF8jIPb33Cvn9qvuUKt0ypdPlGFjiHPr7pnR01h1jkdmf/4YwYqc7VJ60CQZftTB+i4S65cqyNc44oQjpPjAyn7VH2qVfEf8RH0ei/o8LO/BN4knsJcxm30+68CtkKPP4bN+d5P48tJbe5299wb9cuQ158mE2Yc6d2f952O+92UJ9DGO16M3yIIxw2Xa507Jev1ZBUy/4SviL+o7CIMAa/p1l9tm3HW7BnJGHCTBbywwW/p62E1wtozl5IvvzJ+J75Dzuh13d+x/DbgA+GP4DlKlQcLaCHmTXQBeluwJYihALBZjQBcuuwBAXHqxNmIdhnwJyy9WTMTJPs12chok9t10002mXT4niwW/Ng0SVlJYoEkzBBgmdVB/+5J8LsY8bdo04zoMiRdAHxdxm4uX8rhhQ7qFmzjWVtIbAZqJxXXGUgOWsdZ+/etflwsuuEA2qKsyEwQAasqjQ9ibISizusalHCALwLXsy7hP4/YMYL7qqqtMGiZif621GVBrreGweT/22GNGx+gGFmgsxdRFiKvurV+mYPs/4omxiLPYNpzHB+Lny/71s3LQ9BGaTqZvwI2x/PqlfxNcoF1Ropz8oHz/HzepKrASZaZLnwI9OHKu/t0VrgrbNXDYv1wkAc1Lm6kOOxVH3vAcOfT/u6hzV5Z/yik7VKJl38pYC4QLh1pPFN+k/5dxnWwpWDyuQr781t3i1XRn8TTWIl6UsVYe8bVz5bibXOtv8rVRqi7Mp//X15N3p91mQsvnjcp5T/9MmFRwJaGBwgPGytF3/aBP6vB4RT75zB+kQOu60l0DORVTpX7aHbJn+zCJhnPMEtHY/mgkRxp25cv2xjPFP+uq7hXdPQNCA/33YxwQ3R84nQCUAhCdMcCwDxPbCsgDENk0SJZ52dl7ABsLjMC49CK4D1OX2F9A0rnnnmvSKuHSa9Mg4dJMGStYVAFqAOzkNEi48aZigcaCnCoNErlxk1mg+9IX26fkNX0EjBIfDbBlDPTLKcT10ieA+PXXX28OkY7Ipn1iB8AW8i/bFuPG7RyLMNZxQC8uztbNmTq0BQCG5Ouwww5jlxEs7oBlQCiWYeK3YYpmG+FcTBIgfD9MJjhZoN966y1jEeZ4Jv1Ct1YA33w3yIoVK+zuAb3G8n5cRUiq81vlQ81fG05iJ07uvEfBHemR/t+kHeLnjc+VDg1s1XzAt564RO56e5rUa17lkKaoSCe5msJn6rA6OXJcjXy4doeMnpy4btKVz5b99Y1ROXTOSlnwd3WXJN2Rpj3qTXhRDuRGZeapK6WhuU3654je21kG3/G2lmZZd/NdUjYxKMNOCYlHMUROCmePeFSnGxT8Nrztl93vLBXfzOWSP2364BvwR9xj4nln/ce18sg1v5HhgZCQHo5boN5CzbRXg5JgFY8cL7OudydhUn0VsYZdkv/geXLm7DyZt+AgCUe9GtaU+h7p0/tjcUGLnDhrnYR+/Xnx3fCEuv26r7lWr8Xrb5HZF62SeQ9O02svrizk9kjXNdn5eEqfcM4ayVt5q8iU+7sWcLc6NBBau0bWLxytnFijNUd6RD0Q4xJu8et2jpQfHtF7ZNS9Bju0NbA+uHeGffR9wAKNJROrYbLgeotV0brZYiFMJVh977vvPgOacQ+2AuswgAy2YWJUEZiKsTpi+SWG2AoWUCcLdHIapFQs0JBOJadBwlX5iSeeMGRRThboTPuSLg0SFmWIq3BVtszNgCnIvyzwt2NhDTkX6Y+wCNP3iy++2LgfAxhJN8SkQTILNBZghIkCp+BKDRkW4PPaa6811l8b44v1mPP89Kc/dVYxlnYAM3G/uEEjAOVkFmiO2fP1tV9OluqTTjqpy/kH6sar974kyxfvlOsP2iyvVxebXL9RBR0tetNPvNYleu5TkOHXB8LU4ia54ICdUqNpF/77wp/LrW/9p/jd2XnZuHSz/PJz/yVXHuyX7xy3TOZvqZB5G0ZLq77k8QLCgkY1naVU6GTDp6dskcnD6uX/1o6VH539M/npO7criEuBThLqz4r/TPb9/Pq/yU1HRmTu+Ytl8auTpaFGGU8j6R9vXs0BXDKsSQ4/Ya1E9SXwZ9c9Kbf/fboJqcgKpaUZZFzvY+tuuEb0VyvRaq9UP5oneQdGJXdim/hL9f6HSnXV1uSR1vVeaV7h1xRUHmmLeGTdjdfKtD8/Lj4NcXGlUwNvP/yqPHL9PVKrlqEPagtNijhSckU1NVdIH1Ex/YVXvr9Nbqz4kvznpj9IbrHrHWO1B3ho/i+dGFDreUlRi5x10juyVhny31itrMZtmlJRf/s5CuWYTCgsbJRjpm2X0SNqDLBr27xUwq/cJ8GTr7DNZfU6uvBOiW9+TQqLwxIqjEloT64UmhzAXf13yUnfoLmA88pbpXR4rcTWvyDRt/9bfEdek9X6SzX4podvF9/yxzStGfHRORLWlIVOKahdKLuvnCrlv12m4SF7x1PhbNf9vG80kP4NYd+0n3WtYDG1AigCdMHIDLDC7TcTFuh0aZAAgVasVTQ5DZI9blmgk9Mg2ePONQDUmQYJV2wIueg/cck2Ry11bBqk3vqSLg0S4wc8YhmHuRk3ZEDpbbfdZqzkWGKdwuQBcdCAXwQX4ZdffllsGiSYqulnMgs0ZW1aIj4jNj8wludkYfIB8izcqZmogIALi69N2QSxlSUow/XcyQJNKicAvz0fdfvSr+S+DPTtaDgqT/7wMamr13RHCnjnjq6R2SNrZVVdgbxbUyi7WgPSqi93wwJRmVTULAeXNsmovLCCuhxZUFsqdVIrbz/2phx78eyBPtSPvH+P3PaYPjTD8va24XLm5M1y0gE75MQJO2RHU67UajqkJrUOlQQjMqqwRQpVn0iL7lu6Y5g0emvljUfelDmXnviR93Mgn2Dpa6tk5eLtsnZcicw4YLfMOmWl1Owskk2rR0j19hJjDc7R3L/MyDM7P3xMrTKQ75Syikbz4vz+Ov29vrVBVi5YKwfNmjyQh/qR961x0UJpWb3SMGpH9DpTeCstK/1mMScn5CHJuo61Paq/bU9TowLmB2T0VzJ3V/3IB7SfT7Bh4Rr581V3mV4U6rtxRJ9VdSF9UdYZLYxvALcJRYqCVa1ev1fu/8qd8tVHCIlwBQ1El78kbTvXq34SIM2vac2mTdwuU6u2y/WvlsuCbQWa5qxNrj68Rj47qbmr0kLqtfWkTrQeea7kFJV3PZZlW/Fwo7Qt+IXOtoTNyI+YtUH+8dQnpDasxHU6SY1XAtKmv2XyAyPHz1pp1hKul7a3fiHeT3xZQVyCpDVxILv/N//tTml56r8loL/hiZMisvYDJbxTXXI/xAtp5KhaKSnW9HxRn9T/8gop/k4nuWx2a27gjN4FwPv4u3BaY23TACKsqFgOsWAigM5ksIfLM0RPWHCT0yDh/gzwwy0XoAXwxR2Y1EqAUmcaJNq2LNC4B+MKnWkaJCy0kFQBUCHvgozKik2DlElf0qVBev3112X+/PlmQgAwzPmIbWZMf/nLX4xOsEgDiolzxqoOidXBBx9sQCXWVZsGCYDK8tWvfrVjYuGb3/ym0Qf1aNcpAGD0glUZgOoUYpxxBccCj6s1QtwvsdScE2CMFR7h+yQuGcFSfskllxhXcQuQ+9ov09Ag+rf6tZUSaQ5pjz0y/8NR8slxWyTgjcuMskazpBqKvvdJc9QvG+uVuVtaZN7d/5f1ALiptkne++cyncCJy9tbK+QEBb4BzVmLW9qowlazpNLlewp+q5uZTW6VZ+/8Z9YD4HkPvyno8qFXpkjVhQulUN2ah41sMAuuptGwTtSE/BLMjYjP32ZAh9VrU6tPHnxlqjTXN8tLOpmQ7QB41xOPSkzDPZCWloDen1usqhLrJPDLzqYmSJ7UoVLvxXv+9rgLgBOaMv+fvu1Bx5ZIWa6Ggug1GGrzGNCRq29g1g21LdImGxaskU2L18qEIyZ1qZetG+FX7tcLsb7b8Jk4+OWJu3U/Sw8SjUj0nb9LYPaXeig09A/F1v9Tb4Q8sxNSNXmXHHviGnl93lQDePEwsuLVCYXj9FjlpF12l9ZtVUvw8+KdmiAg7TyQnZ9iTXXS8sxvOgafXxCWKdO2SmNDrj7Pc9SIEpKCwnZ9x6ISXbtEIivekMD04zvquB/2vwYSUz37vx9DugepWKDTDdi60VZVVZkigFsALfGqAOPzzz9fLrvsMpN3FkslscXJaZCSWaBxF84kDRLADaC+ceNGQ+zE+o9//GNHV/vSF8CtMwUSn+kvbs/E9x51VNe8dABmYmoRgCZW1d/+9reGgOvUU081OY3tscrKSj4avTjXfMYd0i7ozQrxwFiPmUAgP3KywLwNYLYgluObNm0yxSZPnmzOBakW4myXbfud8Rmxx+2afbZPrJ37OTbY5J2n35bmusRs+9q6Elm0s6LHIfBwDcW88vCaSQp+E9/J1uVbpHUv8gj3eMJBcnDZi8uN1YfuRlQ///3WweZlGNfIVIIFfd2eInlseWKChjLb13xowF+q8tmy781n3tHfl8g76yvknXUVGiPY+VjjFuAPtqnbX6v4A13Bb0R9n9/doNbfTeVqHY7L/KeWZIvKUo4T9+eGBfM7jvEiV1eXcNtDv05hm0mtxsaA3v+8HYdieo9s+WBNx3Y2f2jRe+TW91uvc/oAAEAASURBVDZ0U0FA1VUUiEs+sdVJP/VQQ4u899SCbnWycQfXY3TV63s39HCzRBY9tXdtDIHasVV/1Rerhi4jOXTmZjn7vCUydsJuyVPAxjJOP5/5mXdlxuFbupSlbmz137ruy+KtyLJX9QaonhsOCQajUj68USpG1HeC3/bj8eY6Cb2h34ErA0oDrgX4Y/g6UrFA47Z77733mrMDiiDQgkiJNemHktMgYT22LNDErWIdfeqppwyxFlZLwCM5dq1gucRinOwKDQBNBcAs+IUFmvzBs2bNkiuvvNK4FkMsNWPGjI6UTH3ti+0Ta+KfIX0CpDsFqyrkUYBMiK1OO+00cz6AKW7Yv//9701xjtk0SPQZC/Idd9xh8v3CIn3PPfcYQAoopS1idxHYpBFcp1O5QB9zzDEmpvpXv/qVKUMKKZi56SfnRywLNDHMpEFiMgKrOOWwLFvpS7+og7s4sdsI1m+szQNZdm2o7tK9xdUj1Lrrk2NH7dAXurhagzWqTV/swmrlwNFvW2OBvLBlrBBbZMXr0zij6vp+58C17Qzmdc32Wgk1dc7Kt6gOb3lhppw1ZZMcNmqP6gv96YSKLm0Kil/eOFpe1cUpOZqjsXZHnRRoaqpslfrdnS92P37sSLnzqnkyrrxRmWCTUJtDQVh+V24pk/945OiOvc52OnZm0YdYc1O3lzoYsmtr89ULJqL32jbjQo6LH+7R4bDPWDucKuJZFtldLXmTD3TuzsrPu9Z/KDl6n+uLxDTlzOYl6/pSZeiWbdXfdbvr894MMlbTPeRpb9objHXjdZtSdntC1W5hyUTidRszKZYVZaIbl0m8pfO5k8mgo+uye4I1Ex193GVcALyPNG6tgJmwQAN+eVHAfTlZcKn90Y9+ZIAXbs4AYoiRcGVOxQJtWZEhwwIA0w/aZklmgYa5mDRCtq+c2/YFy691CyY3LhZZiLjIJ4w1FhKvvvYleWxsA3JxsbYCUzYWX4AugqUWkioAMBMHsE7bMXKcWGXr2kxbWNexLJMGCZkyZYqcffbZhsDLtgWoh7wLYfKAcSQLbs6MFzBLfK8Vxm/jtjkfct5555k0SH/605+M6/aJJ55oJiBwmUZsv7Am237hdp3cL1NY/9E3vmuE9EwDXVobk9witcMra4bJGo3vHVvQJOV5rRLIadP4Vb9sbiyUujDEEMC4TiFHZqtaO7JZsIDj9uiUUJtPnlgxUf62slJGKekLrM+N6r67synPTCY4y/I5x6tkRA2tybuzartNQYMVJg2u/u3JcvGJK+Uzs9brVacup+oSjSVYb4kC8KXME/MnySOvdwVpxLZns7Tpvcfj86sKuv4uAbwh2Mk752rSqsmjSjZAOm2J7DnQWq96dHghZTpy56RYpnWGYrl4SJ+FXq7HvZO4WoGzXeKRxLvL3uhhX7SxN+cfSHXj6gLdV4mHut5X+1rfLb/vNeAC4H2kU2vRzIQF2mktdJ6eNt577z0DdgFjNg0SbMzpWKBJgeRMg0QbAFziaYlTTWaBxh3aaQGlL4DgdCzQtAX4JScvS6Z9SccCDdAE9CK0DVhkH4Dd7sMKCmkY4NZaRs1B/ee0jlIPt/C6us6b0erVq41V2NnWD3/4QwOMOQaYt7J582aTU9luo2Nr5bX7cMNmLLiQcz6EfkOKBQM1fcUiTP/Zj1COY+QVtvplHFj2EVvObOg/GL2tDAYW6PLxw2WNrLJd7lhDnrGpUcmHdOlN2iJRKR1d1luxIX28dHSppk1QYOGwAtsBo8ut9b1bdWPRmJSNyW495hflSUiJxJzywCvT5Ik3J8mhVbtk4sg6GVHSItX1ebJme6ksVbfnFrVeJkthFlvR0YV/WLnElTthr0Q9EvzlFXvVxFCpXDxKOSjSOyGkHWbZuPK0x7LpgKdkZJe41f6OPad4RH+rDpl6nsJREt+9Yq/G4yns6n20V40N8so5Iw9Qa4pOzmiMeaaSUzYq06JuuY9JA93fAj6mEw/V07gs0HuMC3M6FmhcnWFWBvgC/BYsWGDy7lqrLGRYWLsBtQBL0hNBgIWFG2KwG264Qa677jrjAg7AxKUaF+1bbrnFWGqxGlt3Z9sWAJZcw6RHIv6Xvi1evNikTwIcY50FoN56663G9Ty5rccee8xMAtB3pC8s0FiUcfnGNZtJCYR+DWaZfNxUWfzkQs111xV09GVMgfygFFUkrP59qTeUylYdXqmeHns3Im/AK8OyHABXHjxWatQNPFlawn55c9VosyQfS7U98RMTUu3Omn0e9Y4JaA701rUf9HvM8XBEcidP6Xf9oVSxYvJo8ef2zYKZW5wnB59xxFBSQ7/H4tHJ+Zzy8RLbvrrfbZA+yTt5Vv/rD5GaOeNnS9smjVsleXd/RJOB54w/oT81h2QdyKxaAvmqzu7PnZQDDmoYyZFnpjzk7tx/GuhbgMr+6+egOTMs0HaZOXOmcXu9+eabDVsxsbNWiMMlzpeF8lh7ST0EWEIAe4iTeZljRx99tMmXiysv+WOxptrURKaC/qNtLI/E8lpXaCzD//Ef/9FhabVlWVMWEIhYFmislLj+pmOB7q0vuFD/4Ac/6LIAIFlwFf7iF78oMEKPHz/enBfWaayquDevW7fO6Iu+AxrRi9UHbtqkPEKs5RcrNzosLy83Lt4QgyG4WpOf2ZJXvfDCCwLgBfwiAGzrhv7QQw8ZKzv5gZ1tjRs3zjBRA7RpH2EMWNch87Is0PTVEmgRA8xi2akZF+zUlOd7sy7cprFB+G/63Bl9jm1zDjNHA4QPO3umc1dWfh43fazkBjLwK02jnZycmMycnbhfpCmSFbvnfO4YyVVL+t5IXkFAZp9/1N40MSTqls49QzzKq9BfyT/4EPFqSIoriefqnKs/pW68mb9meQN++cSnj3bV164B/xGf1peS3P7rI79Y/Iee3v/6Q6RmzqQz9d2v/4Ph/YY2XElowDvhYPFOmK4v25n9tj16Decef56rvgGmgcy+vQHW6cHWHZcF+mQD8InVJU4ZQApIhWGa3MjcXImBBdwD3onzffzxx+WRRx4xuXtJX2TTPAEyiW1GPvUpfblQwQ3aCu7cAGPa4TNWZstAjSXX2RZ1sCgjNr7XurKbnfrPgmxcry2Qpm2nJLs02+N2TVnGaBfnfmc7g+VzReUImfKJUSa+sj99Ju/gp246tz9Vh1QdJgIuOH2l5Gme3/5IruYFPve0Tf2pOqTqnPLF4zvyWPZ3YH59Es79QieJYH/bGez1hp//eR1CP9+U9bY46qprB7sK9mn/p545U1nJuf9n1mzxIZM1LCKYWeEsKBU89euaezav3yP1jpkmvinu79pTOlFircqIkOF16FQ4dajrKZno3J3Vn/FOKPr6nRIVOCV6VgX6K775Ec1FPazngu7Rj10DLgD+GFTeEws0TNAAQdiHyRGMK3BvLNBVVVVdWKDJX2tTCNnhwJSMazAs0KTzOeecc+TII4/sFn9qy2OxhAgLwigst1hd6QfWViym5NV9/vnnTZtOFuhM+mLPwRqLKQzWgFZIqjgvLsrEzBJniwByYXQePXq0cRfevn27iY3mGEAYiyoCqdT06dMNyMWtGjdnLLwAVfZjbaUtmJ+Jy/3Zz35mLMmW7AsdcQ7ksssuM2WIPSYXMzHRWMwh4aIM1lsbH2xZoLFko/eeWKBtv3784x8b3QOWLZA2J9Z/pJuCdZoFPQ8GOfJgdelRC2R/ZOqwZiks2juLXX/OO+DqNO+UE2Zul6pxteoK3ZUMK5O+njN3tZTnvJ9J0SFdJq8gV+bOyO339QhR1qlHFEowz70mfUpGmFfCa0Evb3UprqhAfo4EK9z4VadqVi9YJ9WePE3/piAijUp5QYbHbWuTR1a9u9VZPes/e/JLJHj+raLpAvqli7yLf9KvekOtUmzneok2aMhR3x8zhseNurHq9UNNLXs1Hm/5GHloyYk6waV55pUVP1kimgUDronnVx+hRKCdxK/J5dzt/acBFwDvI91bKyAs0Ha58847DekSABeL55lnJlxILPMy7rcsNj4UUiYAIvWwRAI0LfMyFlKYkXHpXblypZx11lkmbrWystKMgBhZhH5YSyOWUtrBFZpzwgLNNset2L4ks0DjCg0RF+Uhjnruuef63Bd7juQ1LNfoA4ZkCLwgnyLuFtCbLLhJk47JukBDxOUUQCr1aIt44fnz5xuXbsZjhRhiiLw4Rkoj6wLNPiuAffoFKMdd+fLLLzeAH/dwzoFYK7FlgQZAM1GAuzduzcks0HaM9IvvDcCOcA6n8P1QhoVcxYNBvA01cuLIHe1d7byeeuo7lt8pRXUyfVRUQrs6rfY91RnKx+LN1ZKjv7MbL39TvSTjumQ2oeD3tclnT1kpnzpprUhr7VBWUcZjO7Q8LJMLWvpsCfZq2q4ZJU1y8LD+x7Nn3MlBUtAnpI/ry0RcXCcbo5JXpjmBa3YNklF+PN2s39Ug4daorKvPkV1qRYP0HbCr3HVmTY70JrUQb2nK0VRyOdJY6zIWJ38zwRMukeDczmd18vGU2+pyWviv/yfesQelPJxtO+ON1eKJ+yW8PRHe4HgFTKsKysT1eg1t0jpaN96wM23ZbD3QVN8mf5x3lLy+qlK27SmSJs3YAOjd1ZAvS9aPlYfeOEzJLEdI4w73OT0QrxGXBGsffSsARcRlgVaiBZV0LNAAa2JoIbrCCkyKI0A+INIpAHmsuTZ9Ecew5mIBJgcvbNYI1lNrmWUbVuYvfOELHYzL7CNPMDmTv/zlLwsxvbQLOP3KV74ikJZZ4I3VGCBsWbKZHCA2euLEhOvPR8UCzcSETZdk1/R7IEvuyAopVCB2xuht8lp1hbS2eSXqyPPr7Ls6o4tP3SOnFNfp0iixcJ4Ey0udRbLysydfJ3zaIhLwx+TuH/xdfvPQEfLOypHSGuK23NXNHgX5VN9BzcV6/mkr5LTjN7BLLSOuHlFDcHiZnDpqvQSr47KsLl8iaa5FyloJ6oTDIQp+TxjeILnDNZ7LFaOBnPxCCYR26URiSD109MVX0yCluh6tlTigrvjBoKI4vWd7h3WfxMxmtRaVF0lAibCioahagTy66GSBqtOnpgcswhEFwuRKt1JQmm8/umuHBnLP+Rd1wdV74191Mpr0SHrfTCnBAvHklUj+1/8o3gMOTVkkG3d6Civ0d8zMi6Y0U0DrH6m/V59eed0Nl0Y9AN+4lo3s1GeRWjLJx+wpSnCrZKP+0o05oN5HTUoGunzrKLOkKpeXF5fCke5zOpVu9vc+FwDv42/AZYHumQXamVPXgmA7eeD8KrA4v/jiiybelzy7uCXjmo0V9uWXXzZuzRCA/eQnPzFEWuQRhqgKwErs8KmnnioHHnigca2GtZntGTNmyG233WYs4Fhjsb6/9NJLhtCKc8MCTRoqiLlwp8biDmM0IPz00083wJ1y+5oFGjdyK0wQDAYpPXS6bPnrc5KnPlVzR30oW5rzZQM5fzX3L69zHrWskW/Vp1bf0XktMrWkXvK8+gBW8ebnib844cZudmTrv3xeKBIvv15vXK65ZJFs3l4k/3yjShYvH60PVn1RUdAbURerEeVNcvSMbQp810lRQefLn6fctXBw+ZQeMlV2vfWOnFhRL+PyQvL67mJpVL1haYtJp6OTJn0T4n0LVK8nDK+XqoKQ5r71SumMqdl6FXYbd2DiNGlZ9JpOuMR00jGkRIteDc3wqhcRMYQaC6iXLL9vrL6BQJvZNo0oIYyvwk2V4lTo9OOTGbE9OlGoOEQBRrJ4lBPg0LkHJ+92t9s1EJzzZfEfdqY0Pf0L2fPSI+KNah511SV3UHjGwoUjZcw535CAWow9/XSZHqrKzqmo1Buhgl5EAW1km09yFJjlFLZJTq4q0d4iuV+qp0KsUX/vLeZJnqgT03K04UoXDUw5/Uh595GXJYZrRxrJ1fR65ZPGpDnq7t6fGnAB8D7WPgzCyYLFE5dcXJqJ80UgQoIB2imQNQH0sIzi8lulwMjJAg3wA9SRRgeXaKyngDhYoC1JlG3bskADMnGFvvTSS/vEAg0rM+Rd6Vige+sLFl0bX2vHiB6Qt99+W26//XZpbm4WiLGSY2KxHpN/96ijjuogugIEn3HGGfLkk0+aNErE9QKSrbWWtnAdh6zq/PPPN67V6Ap3ceJqAbTXX3+9AchYnMeMGWMALXqG0ZkcwW+99ZaxBhP3y0QGrtO4i7/xxhsGACezQDMWywJNeSzUSDILNPtwq+Z7oi+DnQWa8eROqtLnqUILfUayTChoNkubviS3xnIUeOQo4G2TQDvopQ6C1SNWkrgOEnuy978hQ8sdKfEQpG0JPYwf3SCXn/+eWdQBQRqaA1JSmNo9FzCSU+ZaLtHcqE8eJ+v/93GJtoRkYiFLtexU6+W6pqDUqEtak3ooFOj1OEytlRMLW6UCi2W7eP0+GXXysXYz69dFp5wrLe++pSgtYq5Lv3odsPQmBcedYp5rvZXLpuMjDhguEw87QJa+vLJ9qiv96ON6czznW2ekL+AekXhRhRx7zSppbZkmxd4WGZ4XU7fyhHt5vXrOXKHX6b+d0r944aGsXo9axnOGV0nblqXt16GCXAW4sRaLfBm9PpxTXKXs9VZMUjIy1zsBLTnl5H/5vCx54EXdlVp3lP3sf1/j3hedShtAn51X/wDq1tDqissC3ckCzTf77LPPGiIsYqOx3CYLrseA4GCwKxsmhFIIEwMI4ByQTXncogGsAGK2LRgdq3ktkeXLlxvGZ+KZEQAxMbsWpGPpZdIAa7Rti3JMHhDni1ignszibOO/TSH9Z4/bNfttXDZr535bZ7Ctt7z9gTTFgt2IXYhlxbpWElDX3iTwm9CDRzavbzT6GGxj/ij6G6tpSDw7UzSul2Na8GuK6wtzrHZwxIynGN4+3VV2+MES8GEZ50UkISNyI3JMeaOcObpWLhi326xn6bYT/FI+N9hmLMi2Xrav82cer1YiJgg6dZmJTvKOcCcRUukpStCvSk/a5Jgvz68TpJ0TM6aS+6+LBv79+t9KS3OrRJRae7d6yKzaE5R1dX4B/CIP3/OcvPDMm13quBsJDcQLNGysp4swBfg1NbVOvGCkq8YUGmhct1VG+Vv1iHrGdFEugQ1xM0HTtHZjipruroGgAdcC/DF8Cz2xQHN6QBFWSSyQrHtjgYbdGCZl4loh1rrvvvsMGzEphqxA0ISVFhboY4891rBAz5s3z1hfUwEwJws0pEyzZs0y5FOwQMMmjftwKhboTPpi+2TX3/72tzuYnO0+53rYsGHG/Ri9Pfjgg8YlGcvv2rVrjcUYEirk3nvvFRiiIQSjb5CILVq0yOT/xVWaMRHrC1DGnRpgbEmm0B3MzrathQsXmuNYjSHKgukZtmks+pWVlUZ/NtbYskBjacZC3xMLNACfGGOIzgDKLADpZHDvHP9g+Lzh/xbJzqZcGV8QVpdSjQ/yZNbrD1s0eXyxT/as3izlUydkVmmIloo37ZZ4S5NOr+sANdVlpjq06ohpVc/61+1mVq9jDXUyeliDrG9Uxl0Ts5qpOjwyorhWLSHN6hLoWjjQWmjNMslRb5ZYo07OmJe63n7c+oasF29k/SqRk7uSFGb6LQzVci2NrbLuvc2GfJefucUfVqN2G4gcUTfK+U8ulgOPqBqq6tjrcf31gRfUMSG9N0JjfbM8dPffZe6njtnrcw21BqKbV4mHeF4Nt8n0WZMgwlK3/U0rh5o69sl4Vj30vBIviowOtBrPt5B6viF+DRHBAw4bwOJfPCgHXXz6Pjmf28i+1YALgPetPsWyMdMsqX1g/wXIAcQsCzTHAL24LycLLrWwEWOJxM0ZQHzSSScZ0qhzzz1X464SXxmgj3y5uEEjnNcJgAG5WC85DqD99a9/bVigL7roIgP0nOelL8ks0LgwExMLURSxt1dccUW/++I8F59tGqPk/c7tG2+8UXJzcw2jNv1DcJcmXZR1IcYFGsF6myxYl9EXbXznO98xlmbclK21ljX6wxUasa7UsFJbQfcW9BJDnMwC/ac//cmMZfbs2WYCIpkFGiu0JbWCYIu44yeeeMKwQNvzci7Gas/LOXHjHujSvLNGX+Y8sqmpWCYVKYDQrwhX6FTC1wco2RXKk5a2AFhPqJ/1ALhxp76MKMlQs84eKyGJZBj+jT7jjarEiM4xK4h2Rb11a3ZLIM8nB1TslvU7IWJSJen1mV5g3Y7LAcN3SzC/SKJ7dklgbHZPyFhdte2pbrcA2z3pdMn+hGDtiOzYajfddbsGdm3ZIz5/gmkI2MYVyWI1x9p+btOQki0rt+keV1JpYOeHe9QxgamCnmXVsg09F8jSo/HmOiW2ytHntF6JitN6A8HmOaMhTTHlAJDm+izVWs/D3rN8vSnAu0++ol2WZIk0NGtoTqt6ePDm48pA0oALgPfRt1FVVWWAzc9//vOOFgGxWP+IXQVIAuAQmIghbLrwwgs7ymKdBGxBuETcLimIiAkGGAGaSBUEKRMxrYA3jhHzynELnmissrLStEN7uPd+4xvfEPoEMRTgF2BLDK4V+kKb5L0FID722GPyl7/8xeQMJm8w+wDTkEuRgijTvvTEAm3PTawt/U1FgoV1FlAOc/OGDRtMfmIs2IB64oNhgXZONmCJRX+QWDHWL37xi4IlGaGt6667Tq655hqTAgnA+alPfcq4TNu+QHzFZAX6gkUaoU3AM/ouKyszeYLZj0WdmF5yAGOJJ7aZPMsWXMMWDUEX3yPAmn5hRX/66aep3lHObOg/vkvrJo9VezCIL9+6p3tkXUOpFOsMaFlACYW08xDkIAnCnLgyRPtkd2uehGKJ201cr01/YZ4pk9X/AhozruQiSKxeQXC+TimgVlWi/uS6CSSeSKwd/PLZo+k+XNH3Oay3qsuAEjdVjdglH9YWa25GX7s12KlMJX7R6zPoj8io0nolGVOl6u/btf52XkWevAIlfU1YidrnHvWghWmd5ewne63mFLjEdlYndp1boGEizA62C586t+zeznVuoft77tRG1095+syJtbuTdz3SdSs3zz6buu7P9i2PN6DXngJavS961DqZw6Sriv39Wv3Y33xMWaDjyp1gyvgynJ21jWTJ2pfBtYYBh3SHrgw8DbgAeB99J4CnZAEQffjhh/L+++8bgGlJlAC/pOvBSukULIaHHnqosWgCVquqqsxhyJMQQHSyYCXGrdeCPcAiJFqAWMidAGYAx7vvvttYiAF1EEZZoS8AXKyhgFyAGCCS3LcwNhM7CxDFgolk2hessuRDdsqf//xn0yYuxLA3A8TREefHRfmTn/yks7j5DLjEtRlrK6AWd2/LAk2B5LYAu+gNl2ms1tZiTlkYlm1MMLqHGMwe5zybNm0ykxWUXbp0qQHM6ITvj/NYEi8mKX72s5+ZOGLKMlGA5RZyMoRykGsBtO0Y6Ze10NtyprD+A4xb4XsbDFJYXiBqvzTCQ7VOUxvVhXMlqA9WvzI/AzKiavVtjSoI6aCYTJRva26RojHD22tn78pTpARYkbBqLyFYguOtqk3egQO64E1l35YVJ8eVCytOuFFHDd0mrssV8Q3T66ldVwGNQR9fXiOtykje2Bow6zYlyvHq7HyuAt/C3JCuo50vfspe7C1NTJa5qtR5BE0hFQ+1Gv04X47tizE6cu5nm2NRTQfiSlcNDB83THyBBIjoeqT7Vl5Rrhwye2r3A+4eo4Gi4gIp0Jsl83/phFvm9Aluup5U+vGUjVaPoRpzCGDbphTaHvWCYdEkwYkq+syOw/aui/M5Q11Xumtg3JzDZNfyNRILteuvexEZNm2sTjZkdg9IUd3d9RFqwAXA+1i5TtCH1RYLIHG4t9xyi3F5tmmAIFzCLRmBGRiLKUAVIIgks0AT04vFFEAFaKusrJSjjz5a7rrrrm4s0NS3sb9YNvvCAo17MlZPwBvA7+abb+5w3QY8r1q1SjLpS08s0IBHWKaJLwaYE3972223GTdxmLGZHGAf1lWspxwDKGOtxroKKEdWrFhhrNvoj7aw0hJra0Ekda1l1VTQf8QKpxIs9QBkYnaZIPih5grGio4LNYKV2rpe0wbWZCziWM6/973vGYsvukYAwABmQG9yv/ACsO2YwoP0X279do19iWkaCl45rGiOQbX2huxmmrVPlKyosU5kZHaDDo8vKHvqy6S8oKVTU7x86PwUS0J4sFqIbPcl1lF1Z9vVOFkmdt2dlVsetVDEx0yW+OolHcAtT4nYWHoSjHM5ldP1JdB5HfdUY+gf2/X4Xzq8N5yjTQa9zmN8rn31NRn57VbJCbpWTKsbJnfPuGKOPHHHc5q+tpcJAr0Wj/3MEbaqu07SwIu33CNHKlBLmAOSDrZv4iRT/s5qWffiYpn4SVeXTi01FBwiwbYV4tMY4IRYsOss1f1zVOOGQwUzxM1k21U38XhExn76OXn/fghSux5zbk3/5m59f/1Q3yHdyWqnXgbCZ/epv4+/BQCcXXB7Je4TEAkhE2mQrPBgtOUgWsINFrZhy15cVVVlimJxpSzWTwAp6YCwGGKhZE1sMXlpAWtWKG/TILEfd+Grr77aAEfYj3sS+omlljYQQKSVvvQFoM+YnAvg7/XXX5f58+eb2FjKADoBhIzDAlcmDmBrhiyL1EXWagvY5FhlZaXpEmAXQA6RFW0B3nFNZoKA/jvjbKmwceNGMzZTOekf1lnG/s4778gdd9xhiLTQIVZhXMkBtRBXMQbWWNcBuMRsMxGB2H7SD/SORdv2C8BMexyzuk3qwqDZjKi1O7DjA2Pl7WunycM6Mr9Jdjzzj75WHXLlm6trZOm7o/Sa7ml2OPE7TDV4rJpL/tnLS3WqikN036ItwyTSTkKS6RDbtPziTe6rndVXpHqHtKxYptekz1h17f6e1sb6q3GCsUhMGl5/paeiWXnsC987V2dQFWxYF4UUWgCSfO2uS6VomJvCJ4V6pFHjf5c99KJUBf1yilrKEeedkTtonj7zLy0vlFxNsjzv1nsp4opDAwtejisQ6/srP3UWvtz5fuloMqs/hlpfluCwXDnt0UToh9fhee9R06JfE5yccFeBDD/MIy1ND2a1rgbq4Pv+axioIxnA/bJWSEu0RFcBSA8//HDHApMw7MMQLWGFBBxj2bTMy7gBw3h8/PHHy9y5cw2T8D/+8Q9jncXNmnhUK1grv/a1rxmQ/Oijj5rduELjqmvjVG1Z5xoAiLsz6YYgvoKNGgvm4sWL+90XZ/t8xoINcCTHr1OwGNsx4M7NNuOdM2dOB5EUZGK4kWOBRgCTCHogfzIAGddq4m8BwYBrK+xjTOgvlTCxwHj5DnBxxt2c2A0s1TZ3M27OWIfRE6mSIL2iz+QJRuxkAfsBuUwYoEsmHegXAJg2BrvULlysMS0+GVdYry8hdjY5k1Fp7KUvKsW+Ftk9z31R3vrGUtm+c6R6fyjjbh/fLwAdy1eMk5qtzdKyW63pWS4Rdb9dvbxBVu0epjmona/G6RVDzur3lDBr5TtKruOmnzGKanjzdV3zoqx+GpGeJmYSeuU6ZAmH/RJrapS6V19Mr/AsPVK7u0HqJKpM0Jq2TBcnEGabpUmJiRa/vipLNdT7sNdp1gHyziPT8gJyUVmBzND1KHUtHaskY0cWBOQLGpZT7E280jbuqJGaddt6bzhLSoTqm6T6g13y7prx+n7Ut9f+JasnyM411RJWMidXOjUQbp2nG2EFwTnymZf1nf2buTL6RJ+MPM4n064Iyif/t0hGHcc7qBIERpbrfdLEL3U24H7a7xrQeQpXPmoNpEqDBLiCJMkKoBD3XkDSDTfcYFxuLQv06aefbgiUsJRibQTc4R6M5ffHP/6xiYtNZoFOToOEJfKmm24yRFhOa7E9vwW/WEBhjQaAAtyTWaD70xd7DtbEHOOqnEx8hYUV8G9TBOG2jTs5scS4hiMA3ttvv73DhRiXcMDy7t27jeWXMujxmGOOMcDUtsX+3//+98a1+ktf+pK89NJL7OoiAFaszpdffrmZnOA7QLDiwyaNWIALmzdkYU4WaCYqbIolxohFGzZnJws0ExeAdWe/aBdLtnXrrqurG/As0OHdChhaQ1IUCMv4wjrZ1Ni7BQ3Lr19jMCeX1OjkgD429iRikRh/tkpzda2Em0PywrxD5Aufe6NHJm2njniBWfPBKHl3aaUES3OkZVet5JWXOItk3eeGnbVm0mnxh6PNdTZ12B5znaVTRFitGh/sKZNl1SMkqPNojdX1UjJmWLriWbM/srtaYu0cEZGIkuaom18gkMhNy+/WCqAXadNYwlCIl7zEwcj2rWa/+69TA6vf2WBiAOtEmWBVTwFdvO1ThxF9OQ4DgVWf777hAuBOrXX9VL1io0Q0/6+V4Qp6T9SlJ9mzdpuUTRzTU5GsOcazJkcNI2s2j9K85xE5ZNJW9eDqefgRfc68u2acrN0yUoIlOUIbgaIEkWvPNbPjaFtbpzdlQPVz4CW5uqQeO7943KB9vsrUBdy9+0UDLgDeR2rHZRnrnjMGGKsjFkRYmp1pkCzzsvPUpExiIW0RrrMI7sPUBegRNwsQg2wLK65Ng4R1MRULNAA7OQ0SrtAAW6ywVugLFuRUaZBwIU5mge5LX+w5nGtAbklJ58s6QHPdunVmXJQDzHJeytD/b33rW/Lss892pHyy1nTK0hYgE2ssMbeAR3SIqzOWWdsWoP6hhx4yYyHPL5MDsFk7hXq4Ph922GEmXhswC/ifMmWKOQ+xwJwP4fthMoFzWBZocjg70yABzJNZoLEIA4Btv+z5qYerNdKThd6W3+/rNp2Jb6ckLlVCIW9OjWxpLNZ4YGWY7BITTE8TrLvFyhBtLMb2oWvfoPf7YPZjB9rZYUOhgNz/5xPlrDMWS3FRiwKOBDN0qp7hLr1u/Qh5a+GBicOKSjJhRk3V1lDa17qnXsL10OPkyFtbx8qOxgI5fNQOyVWPA8SrsYNcn0iLsqAu/nCUbKpP3IeiTS0SqtOcty4A1jQpCX0ZRem/aNSv9ySfTljiVUO+b7VfKljDLZL9sSSX87g+m1zpqgFnyEtU74csKSXN7pRls2xnTN2a+yowmbuS0IDRRfuzd+kH46WhKVeOmLZJDS4xZcTvqtuQen7w+1688gDZ+KGSC6p4lCiQ7A2uODXQl+tLlW/TODibcD/vVw24AHgfqR9gisvyAw880K1FWH+vuuqqjjRIWAhTCVZRWI4BzaRBsoJLLrGvuDBjoUR+8YtfGEswll9iiK0ks0Anp0FKxQIN6VRyGiRiWwGJgEcnC3SmfUmXBsmmCKK/gD2srlhWbeohJwAkJhjgauOWsbjiwmzjpG1bvGBgQcbNOLktxgahFe0zJlylnemTrN74fiyTt7X4cozzs5B6ifMhxHIns0BjVbd9pxyAOpkFGgswYsuZDf0H6ZgVcj4PdPGXlxn7hb39YwmeVrZLWaCDUhfKVSIsjQdUsOHXh2uRAt+SYMiwQzvH5StwZ5JzhxVrbsCgRBpbDJB45tkjZGLlTr1ON5pZeqsv9EzO2j17CmXRkirZWd1pcY/rZESetpPtsvy//qQqsLMrIhvqSnUpMddeWW6rAcIwku/RdFz1OuHgLMvL4fv/9WcZ8Zubs12N4h9eoXMI6iLpeNklnRlAOAkbp9SVrzzxwpzyYJburDxobO+TVHrpTj28Mks11PuwyyaObn/mdP7Ge6oVU1aiorHutWh1xDOCZ4WVDdsrZNOOchkzvFbGjdgjBXlwSagrfktQtuwcJtt2lXaZ3Irpjz/bvYys7uw6xztMf9fb7WaPa51+VQu83ltdGVAayGoAjOUR0Gmtilj0YFUm3haCKcCjzd2b6beGxdQKoIg4VhiZf/rTn8q0adMMKRLHcXVOxwKN22+qNEhOoAQ5FJKcBsns1H+WBTo5DZI97lwDIJ1pkGCghpCL/hMTi9uzFayYSG99SZcGCaBKXCzWVED8ggULDHmUjY21KYIg/CLPMeRduCGTD5iJgVtvvdWAT/qQSVtYYYnbRff33nuvmaDAOo5AxoVlHkCNBZYUVFiIreDmzPWB6znWc1yXEVzPnSzQgHgAv+07/aL/6C+ZBZr6thyfB6MUjC5vtxR1vozgHlmqQJeld4lLXlFn3d7LD80So48+qMvA4mo9X7t+lFkKClqlUJeguqs1NQfVuyRfJ9i6366DpAYZVd6lnWzbqFmxTnYtXCYBnSRobc81ndCBpufSCRmWniSY0yY7XlssdWs2ScmBE3oqOuSPecv0t633rf78Ormr5uS7uYCTL5IxlRUKbqvknVdX6DPBThsmldLd533t1KSd7qbVgG/9UkO6aL047P50a080LN5dGBqq0hXJqv155cWSX+LXON7OYeO9Adhl6U0KSoOSW+b+trvoaWuJxEs3a9rC7s9lZznzvlmtySDLXf059TIQPutUb3bKtddeK5MnTzaxtWgA6x9uxZBREXOK1dNaW/uiIayxdpk5c2afWaCJj0UAoYhNPYRVkmPEAH9SXXCJA75C89ziZm1Bqamg/wC0AGxiea0rNECO3L4W/NmyrClLvlsE4ijcj7FSEjd8yimnmP3860tfcLWGdMq5AAxZcPmF1RpGaJs6CHBLjK9NEQQgx70YtmWbigiXcFyNcXFGMmmL7xW3bVIV/eEPfzCWWVNZ/8HODeM1FnnGi86xOtsFXaFf+gtAt3mcOa+TBfqSSy4xerWWaeKpWb761a92sEDDTk18Mt+bHaPtx2Bbh5bME7/mWu2vkCM4UL9e89pmApb7e5aBX694wijxFaa2hDepi9qOnaWyaXOFuswXpwS/vEpPmHvUwB/oR9zD9X+dJ1HNLT1cPRGINe+LqGOfDA+GtX6rbHjq5b5UHZJla196sf+eenpB1i9aNCT1sjeD4vn6o0e/bcCvkwDL2eZnrj5eDj1uqnOX+7ldA2ENi2p69SUpCWhu6nTu40naGpdfJxv/+3+S9mbvJu8ykyq2aAqkvj+3qTNxxJaU747ZqtGY5klv/NXfNK+yUtu1G1TS6YL38dZ7lFl//Yp0Rdz9+0kDWQmAsU5iGQTITJw40agewFVTU2PibLEEX3DBBYahOZW7bF+/K2thdrJAp2vDusdWVSVmLgG3/ICITaV/559/vlx22WUGmGGdxoKZnAYJsOtkgc40DZIlwgJgzp492wDNP/7xjx1d7UtfAM/OFEh8pr+kG8KSSr9pG8ssNxAs2YB7xgrYJL1RsjswgBgBBCOZtIWVGWs4kwdY9Pls5cknnzRxxriWP/30010Wa53HbZpjjMdagOmjU+x3ZvfZ43bNfsZoF+d+W2cwrZtfeVZKi5v0u+ob2EiMUcGvPyq5BV5pWbpgMA37I+lrzsjRhvyqP43H9XUwb9IB/ak6pOpseu51495XpPG+QXW7x5UvM1FWcrX+Fmp8a1xjDDc++3pm1YZoKe5P9fNf0/uUwoxMVdiuC8qr4dgQaLVu3DBENdT/Yf3licdkeeBFqZXt6gwZ0ThgnXTRdUiaZINnifz+yV+bZ3r/zzB0a9a+qZwlenFNKqk1ued7/n3HDTFjRV6LtG7bLqGd1UNXMX0YWWzHBqmq2KHxvsT49+XHrfdIrTNx+IcS27mpD2cc2kVD7+ozp6FVWn7wrnlnjbczlDtHTWhNvLVNmn/4rrSt2iVN/0xkZHGWcT/vXw30bLvfv337yM7+3HPPmbYhgwLYIJYU6Ve/+pXZxxpLIGDYGRNqCvfxXyoWaNxjcclFzIuHgj6AHeCPdDzJaZCIO01mgX7qqadMGiTihukngNBKMgu0dYVmzKkAmAW/lgV61qxZcuWVVxoXXlyQZ8yY0ZGSqa99sX1iDaAEyALmIYPivLgPI7g7I5ZtmQkKp2BBxUJt2ZYzaQurLPokHRT5e53x16SbwiKLOIm52HYCYHvM9gvCLYi3mIzAKo7XALP8VhgT/YRUi3NUVFQYpmeAMksyCzTfO0RkCLHDA13aandLXjAqRfkhaVD3XFx3MxMevBqvXdao7Dq50rbHfTmpqw1JWGOmc3WWvTdWzmQd14Z80lDt8GlLLpAl2617EmmgmJc6IL9ZVjbiapa41tKpQKnDNEY9LpVa3s5ntWpe5myWmN574qGwAcC46uo8akYC+E0siftAeMeHkntAZUZ1s6XQww/9RcLRkGzM0RdmvV/6JVevwDYFweoFo9etN1yuHkhLZebMTj6PbNFNb+NsXLVG2pqazf3xCAVx6+pLpUbDGsyki/p88FuH5I5p6TEFDTK6oMk06dFncvO69RIc4cZetlVv1neSuJw0Y408+/bBqrue74+J7yRR5rQjVojPXyht1ZvEO/KAxKEs/x9e855afxPP3sZr3pLA+RPEfzTvqzp5qPdOj88jbWsbJPyXjRLbmLgeQ8sXZrnWBt7wsxIAk3Lm4IMP7gC/xKQCjACdFhDDRIx1mGOZiLUCZsICDfjlBoRLbrIA6n70ox+Z2FObBglLKBbrVCzQsEYjNg0S/aBtlmQWaJsGyfaVerYvqVigIeLCkkyMKyRRgPO+9IX2Uwnju+222zpSBAEWcZUGKCKWbRngyTnXrl3b0QyA0rItszO5LQAt5FO2LcpY8irqQfbVmxCXzDXCRIET2Np+nXfeeR1pkIjnJYcwExC2X5TD6g/4tmmQcDE/++yzzURLMgs0lnCbminV5ERv/f24j8cjCdflspJmIZcqxBnmLa7HjuhLilrnRlfU6bWt16e6lscaEhMfPVYb4gdb61ukPuKXctzCVT8WjPU0bH2+Sq3WId61qToB/noqP9SPxcKdzMVMIhxU2CDbWnOlQYmbeIVLvBontIALJS/KWItHKzmWU99RTe2VzdJWXyceiP5aFQgrSGtT0hw7r+fUk9WReYfWDdYxm3tZN2jHla4a2K2p46zE1XMmLEk5VVVvtTXu/dDqyLkOa0YGK1yHWIJDylJcF4ZwMcdA4Dz9PZco4SJkgVYAIpFa91pEH7FGndzTa6xYeSXOPe4d+cfb0yWknBJtsdSzXD71jMFTa86hqyVIGjRlMI43ubq01xZGgA5pUn+O+9dJ+H/Xiadc34V8GlhTo8+SEN5InRJrB8yde9xP+1sDWQmAUTrAyoq1CDvJnrBQ4gpMTtlMBLdjJBMWaCeocrZNG++9954Bu4BPmwYJ63Q6FmhSIDnTINEGABfrNXGqySzQuEM7XbHpCyA4HQs0bQF+P/3pT5sl076kY4EG7HI+4nrtuYmzZXxWLGB95JFHZOHCxKwZ5YkZpq4TwGMlZnKA75OYXuJ4neOjTcrDBA2DtRWs0JzXWoDZj2UXJmkAMILFGOuxFdsv2iNeGWstLNPUY8LB9suyQDvHCDu3bcuWs+3ifk9fEOLQB7p48grUUpTIyTi8rMk8IOsa8gzzM+6TXcGwAg4Fd3lK5jSspMmAX8bn8QfEWz5yoA/1I+9fwfBiqd+2W3Yrg3ahL6JL4jpIZQ0G+MKuDfgN64sLkyWl413rhi9fvQkc4JWX5LF5raoj9VDQl7xmnSiIqt58Cjzy9cWuSF/sIMxKloASimWz+PVeSmyblQQI1jRmKXRFGb3ltQNffvPtoheuf/gIu+Wu2zVw4JTJOpm+PK0+wppBYtLkiWmPZ/OBXH1nMTNVXHDtEtSc8iPykiYR7MH2tcfnlYCmI3RFeV7KVIfts1mkPTrzqPflg20VuoyQSJR3V3Sb+B3D7zF5zE5dqpXrox3E5Xglp8T9XdtryVsxWvWpeos5YqpVhfFd6SdRvaVdPRptW+56/2kgKwHw9OnT5YUXXjD5cLG42jhXQB4CmCFWFKBiyaEy/YpcFug9Qg7cdCzQWNhJIQTpFa7Dd999twG5WISJC8YKb12fAb/XXXednHHGGQaU8p042Zad3wlAdvXq1c5dHZ+Z4HjxxReNRRziM/L7Qo4FKGaxYlMbAdLnzJlj3JdT9asvLNCMkfjje+65x0xKcK5kFmhcu62kmxyxxwfC2j+uSkKOGdCigpAUqjt0a8iv1uCARNWll3cVLL656iqdlxvufJC2D8DDi/LYyoEwnP3ah/Ezxsn299ZpHzzSqBbLJk3Vk6cxqXn6gudVwAYQBvRG1MLWoqC3VXXb8aKiZcZ8onK/9n8gnLxw/EgJtbtBO/sDyC3XiZdyjbXMRIoOGJNJsSFbxuPzS2CE6nLzJscY1VncWHc7wUfnQQfwbd9JLHXuxM5Jw86y2f3p/ylR4t+eeEZzUTtemB0qKQ4UqffZeMce96PVQMmRh4n3wQJpa9DQmT5IPByRokOm96HG0C3qGzdVva4674MA24Mm7DBLoz6zQzqpigT9OglrUiJ11UUcVu3xCQ6Wrkeycyt3xrHSkP97iTdmaBUPBCXv6LnZqawBPGoCKLJOsGgCco499ljjqoq7KwRMuLISnwl7M+7DkDbB5NsXsQzQrF0W6O4s0FhxST+EazBgmO+B2GbAL1ZrxFpacUPH3ZjvAUB64YUXmskJay11fi/E29JWslCWGGm+W1JbWaZmJj7ohyW2sv066qijjDUaEjSYm539clmgE9rN15hwSbIKYXXLy41ofG+TjBper67O9TKivFGKCzUe2s4iO7+ceEgCExPM487d2fZ5THyDBBTIWsFBt7nNbyzCO0N58qHmrd2p8W41kaCCX+YrO0EHcazDYjts1axdV50zR7y56rq7F4IVuVLbyXYpOOKoNCrgukteuhf1lZaJT8NUXOnUQOOeRnngoj/LIZ7uACLhwCtyTO3h8vxvnu+s5H7q0ECJxkV7HZPEHQd6+qCecGWzjxVvfufkck/Fh/qxnMJS8R2QejIAwFuupJYsqcAvuvFXHiI5BSVDXU0Zjy8w9TDxjRhr7OaZVPL4AlIw9/xMirplPkYNZCUAJv4XCyVrUuSceeaZhkkZl0LcXrEOAs5wmQUA7a24LNAnG3dugCwkXFg8AZpOIW0ScbQIuZMR3J2dYoFvRN3FnELaKiy8WI+TBcvwn/70JzOh4TxGvC7ftXWBtv3iHADpKVOmGBdTZ78sWE6O0012abbH7Zrz2rhs1s79zj4Nls/54z3i0Tje/opHAV9hpZJ9gZqzXIo2LtDcyaT36Hqt96YWrMMHDauWlvkv9lZ0yB+feN5c8Qb3DgDnBPxS+ekTh7yuehugf9SYjF/qktvC6yNH8wi70lUD/3f3C+LP9ctU72Q5wXu0VHjKJU9JsAqlQA70TJRzfWeot4dXnrj9rxJuCXet7G5Jjj6Pq667WryFfQhRUO+9qmuucrXn0EDB5/4F6j/Hnsw+xsQnBZ//bmaFs6QUBGsF51+T8BzvZczcF/3HnCPeYXuPJXo5lXu4jxrISgCMjshvC+MxMZyA4ZEjO+MRIZ8CBGMh3hfSEwv0vcoEjQs2rNMXX3yxiWXtjQW6qqrKMClbFmgYhC14tP2FBRr2ZtyGN23aJLBAk+4nGazZ8k4WaAipvv/975uY1fvvv18WL16seUgjKVmgM+mLPQdryMZwCbYx0/YYbs+QRwEybd5fxgXbMmCV8eFGjDjjhTlG3C4M0ta6a9u0ayzKHGPs69atM7uJyf3Sl77U0Q/bL+K+mfyw/XP2K5kF2varJxZoSNR27NghP/7x/8/eecDHVV3rfk3RjHq13I0l405zaKYEU0wNgVBCEggtJJTLhcBNu3mp5CWBEELKS8JNbgiBhBK4oYUWSCDmEjDNgDEuGNy7bFldI019679He3Q0mpFGpljWnKXf0Wl777P3mjNzzrfXWt+60VyfPlggbfvHPQizNUs66LdlhtPa07xEyurblekwvgvd0pjCgMZiTtQXvY78tl5GW5T0JtwlR05Uhk6HFXgwpQKWIX2ZPWqHdK1cNljxEX8+WFUuMz/3Cb2vhv5yh3I8ai3a5/JPSrCibMTrarABhjSMRPluTAjDYGXTzzMlFtbfOlf6auCl+1+SSFdy0na8d6wc6z9STis4UT5WMF8O8M+WgKd38mblwpV9K7t7RgNjTj1ZJl5wbk7agMjtwHvvkKLJe+VUPl8KFcw4VFpaSntCGnIbNTaI5mbNljHtoNwq5FGprrXrpaOz1IwYkJsuHOO3tCukoU3vrEk/7e4PAw3kLQAeSPek/NnVWEzYmO1yzz33yPXXX28ALpZkLM1WsATCAs1i40M3bNhg0gT98pe/NACMdEOWeRlQfuKJJ6YslpAlAeLq6upMk+n5irEy4sYNkLvhhhsMsIIF2gI72w/W9CWdBRor6He/+11jDQUQE0e7q31xXguQa9MKOY/D7ozAkAwAhWQKoIxbNGCePlhrPIARod8//OEPhZhuygwmS5culW9+85um2MSJE+X0009PVbH9AgDb3NCcdPaLMohlgbb9wkqMGzeAGKEcVn/6i6s37tR8VrBAI4zRKbBWM9nCQmz0cJdE+2Yp3atTCkcp4YMSXOUulPVI7aGN4g0qQ2++A+Ad2wwZWLGSMp029R2jxsEswbBz1mrqntOmrTQG9FhzL0Nq7p/DyCu5/7WflcLa6iEDN15SyqdOkn2v/NTIU8oujCjcsE0twMpiOoS66DC5KHN0azI1yBCqj/iizVtzY3eOhWPStCW3siNeaRkGOPnfviBTv/k1dbEvE08w2K+Er6xUghPGyQG33SKls2b0O5/vB2LtHbKjabS0tRTl9DsZi3mktblYGptrJbYHpGf8sD/f8NbNEo/EjT7DmpIwrvqyv4VMHEQiPuloL5RIuECi2xs+7O6518tBA3lJgmX1gmUOgIr7KyzIgKNrrrnGMCfDwjxpUu6kFFhCATY333yzbd6ATdLxQOIE8RIuwAjgC4sjMa1WsPxhYcTtGlbjTCzQAOAVK1YYSyIM0QDldBZoADHt0N64ceP6sUAD1hivFfpCm+ks0FiLWSD1AkxDBgZJmGWBHqwv2VigscZaV2bcz7F6o7tHH33UdAkLKa7JWHRJcYTlFndpdIvllP5yHiG3L8RXxPjatnCFzibE8DIZgO6feOIJ+awSk/zmN78RmLFtv5hIoC3y+9IvQDZCv2xsMkCV+GCs0rBJYyXGwk4ZxLbF5wgjNZ8H1njnGE3Bnn/cG5aADaA/7KUo6eZYtW+LAllN9L6pWAk2BplLU0Isn1p+aw5sUpc21Wk8Kp7CqmE/1A+yg77yyhQxSaEyb549Y7ksbhgt61oq9bKawsOkqFB3c7X4+jXm2quTDbNHbZdZNY0p73FvSXIG+oPs557QNr9RU887Rd646XZUl9LPQH03LyvqhT/lkycMVCyvzhHDi8AAHVfzharViF0n95L/e34aDVhOsr+rC7Qbc+lUkdkuKi+S1oberBP9CvQc8Gr6lJLK5DtCtjL5fnz8p8+S2lOOly1/fVz++r2fSLVPCQL1+96p7wlnKzgedfwx6gnSa1HPd305x8/EQULfUbZsrpbOzoCMGt1qGN5hebffb77TkN6x7Ggol5bmUvEWxsSv+nWlrwbMb6UqDp11dymRmCHQ1x0jPT+cPXteTZfpyvDTQN4CYIDOhRdeaMCO/VgAMCwwExM3etNNN5kUQPb8QGtcptOFtnDjxfIIwLQkSgAwrJiWfdrWA+wdcMABxiX7M5/5jAFgnHvsscdMEYBSumAZxnUW5mVkrVoaSeFk0yABzBYsWGDGdMQRRxhg19nZmWqGvvDySCoeQC6WS1IxYVnFEgtBGNZhrKxIrn3JxgINWCRVEVZPgDg6IiaYviGWIfm4444zTN0wQbNguSYlFS7MWIfpN+CVvMRYtgGs1n140aJFpr8QkTHpwHj5LJ1pkLDYAtKfffZZ+fSnP21A7GD9stZz2rGM0fSZiQKs47bvuY6Rugj1reDOPdzFUzVNEh4FvPqCXDGtXYLVYWl9p0xniZPsz2JYY/UBoIDNxgqXTFKCjcmd4vX3PCBIH1DSG3Yw3Mf8QfRVwMIXAABAAElEQVTPnxYvCQieO36LHDC6QbZ2lEpbd8CwPpcqM2eV5qwdpZZfgLBTAuP3cu7m9Xb17HqBXTwe08mCHjXZFzunYixw00l68SnoqJpV5zyd19tFU6ZIy/PP8RaswFYtwaoscidnEo6mpz0L6KSrK301sN9x+8r2Ndv1vuSOyy6c3/sQl0E7u4aSZwrUW2yv8z8jnzvzdDn3k/8mhx9xkPznN6/aZa+9wa43ks4X7TVROla+a4BtS0uJvh9pBoeykHr6JSfvsVq2a0rDzk41MpiUhkpu6bqSZ7wFivc/SInB/qw5lp1eL32Br6no80vpIcn324wNuQd3mwbyEgATE3vxxRcb6yKWVhiCsYwigJxbbrlFvvzlLxv3VYAZLtG5yn/913+lihKrigWQONxvf/vbxt0ZUIngYv3rX//abBNfCxgDqFoLJmAMCyTA7u233zYuvlgmAY0QN9XV1RnmavoKKCWe1SlcE0slVmxcoQH7gFpr0XSWpS823RPg8mtf+5oBqAB3cuta1+2h9AV9YoF2CsCQhby5gF7ii50pggCrAFME3TBWZxokAC8TCqQyAgCjNyeotdei34yZ9rHA8xkvWbJErr76ahP7zSSBTakEWLYAeLB+WQv+U089JVdddZWx2jKx8a1vfctYta3HQK5jtP3d09a+qadKfMkdGtzSZLpeWBOWQrVKRhUAdzcGJNatL85qEfYVxaSgRHOuVoYVCPcdpWf8IRpDnLTk9z2TP3se/R7zEG1f+GyfQQOE6yoGT6/gLS6R8mNP6lM3n3eKutWlXK3lALeY/udVBA991lYMaNOdJKRT67pOxBSFd9jTeb+uPGqebL/vTzqZZcEa8Ddp5UgqJ6m5vlpNnuE7Xn38iXmvw3QFnHTVSfLCfQuls7l34jm9DPuHnnGIVIx2mXYz6SbTsZKSYvnrE/occiVnDYz5xMdl3a9/K7FOJaFUgNvZUWiWbA149Z2MOq7010DBlNnSvrNLipR6ItNEa6pGLCqJ6b1GjtRxd2O3a2AQv8Xd3r8PpAMQK2Gxe/jhhw04AlA55dJLLzWs0IBF3GuHIjAR2wW3V+I+AZGQTBHTawWrqy2HpRKXZoinLJFTfX29KQq4pez5559vLMk2nQ+AjrQ+xJmSl9ZaP6lEeUCtjf3FxReXYqy5uH0PJPTzrrvuMm1QzhI/sT2UvgD0GZNzAUACsOkrFl4b6wuYpL+co+/2s8Fi7kyDxEQEnwkkWVhNmVRArrvuOuNejIsx7XAd9ET7WNohOwNof+pTnzKWcj4XziPWHTuXfuF6TdusaQsQTzolUmghEyZMMOtc2jIF99B/nvE63h43aOcQ/Ap4SyaGpHxvjfOe0WbihIMKjtPBryJj8R10lbNq3m7XXHi5iKZI2CXx+qR8fi+vwC61MYIqNf3lLhlXpm75ypANQAO4qROvguHehf0eaGxGPq68WXYq4HMlqYGSfZSUqVBfjnumCPrrhekElv7i1TCHmsOm9z+R50fGTh0rYycMBGx1ykbfxI658Kg811Tuw0/oO0R8y1sSe3eBxNYulPiOVblXzuOSY89SssBSdYW281gD6MKUUT2PVUu7K/01sPzOJ+Sd5gn6zqq/lgPoc+mWMbLoV4/0b8A9sts1kJcAGEBE7KYFLpk+BQAS1kOsnu9VMqVBAgTee++9qQUmYWKQIVCCeAlw7GRehiCL9EBHHnmkzJ8/38TDPvnkk8Y6m84CjRX7iiuuMFZP4mSRwVigKWOZoGEuJhYVNupMLNBD6QvtOgWiKEAuYNoyJOP2C3AF+CIQUSG4cj/99NOGXAoSLqzj1MWCixV8+fLlBoQec8wxhlgLci3OA3yx7KIHjgFSiSFG3+gQoPzAAw+Ya0ybNs2sc+kX/cOdGj052alfeOEF04adLMilLVNhD/3nUfdn35x/1x/9zC/CAw2LB0XCWyzeuvkDFcubc8X7HCARn8ZQD/AAzaYMzz6Hi7fIjRlEP7GOdulev05Gl2k+S3UVH1wSMqGiWWpL9fu8aqXElV/AFfV83vSqjD+QUAVrAc5dK2NmKSHb1pdyr5AnJRvX7VDC+2Yp1tRxSXoxvuzGrm7WBfozWqI++y/8cUGeaGTXhxnfuVa6779KQj+ol87ffVwuOOtKueXqC6XrluMk9ON9JfzszyTRNXi89a73YM+u6dMJfO+80wcFbTyPmCwsOPvzbi7lLB/5qocWSFdnQl5dP1FauwolqqFf0ZhXl+Q6FPbLMgW/7d2F0rRynYQaB/fqynIp9/AHpAH/B9TusG4WYGmJlLJ1FEAGyMLK914lUxokLJmQJFnhOpAncV3crwFvxPcCuk466SQD2nAPBrTTLwA8ll/S62ClJqbZxtHSJgzFxLdaV2hAIbGyxBY7rcX2+hb8MjmA5RgXZoA7cceA4c9//vO73Bd7Dda4LmO1Ju0UDMkI8cWAegC9Mw0SbtmQcBHjjHUVV2ys9pZtmbYypVRCl0wk0BbbxOuia9yqrc4tozRu4kgu/bIAl37cf//9Jk4ca+9RRx1lUkTRz1zbct5/6Js4ZwSg7kzJZQ4Ow3+eypkSDxcq4UjIPExz6SIPVWZL4/5pGquZ5hOdSwMjtExrpFIqE2q55KU4hzmFuOqxoysopQX5TSLmvB2iO5UYTH8Xkb2qmyWgaaK2tmHpUCuwEjpZwTrsUb/o8eWtBixz3ONXls6mRgloDty8l47tUljeLXsd1CprX5qUmzpUn7V775SqCU2SaN2UW508KrVlxUYzWg03l1Jd+P6y8FXXkPXUd379G2vySCtDH2p08QMSfvhazbWl2RhiETnnvnHyyma/PPVOgYwNtsmp05ok+vSPJfbirVL47wvEU1o79IvkQY0da3fIusbRUl/eJEFf1JArch8iyXvTo9wTflnfVimTVm5OnnD/99NAZ0MyBCymZJUrto3WVIYxKQ5EzHO8K+qXUKTXs8urz6aOjRr6VzOQJ0i/S7gHPmAN5CUAhmjq5z//ubEg2tjXdD0DHrEWYonNRerr6w1AdMYA415LWhuYmgFcNpbWMi872w0pzTzLRRddZNyDOYf7MHWJ/QXEnXHGGSY/McRRpEGCuAqXZspYqatLskADsImDpT0AFuAPV+hMLNBYkNPTIFFu/Pjx/Vigh9IX2yfnGmAKYRdAFHBP7HNLS4sZFwAYAGhdozn/5z//2ZByTZ8+3VhwacumQaItm1KJfqELLMZYl19//XXTFmMABBP/C4hnrJT7/ve/L1iOsQ4jtl/kY4YVm2tzjvZsvyiD4L7NZIKTBfqll15KAXPbFmN0skDTL9sW/bICuEcHiM1TbM8N27XXL4nuYgXBXUoEocCtF2Nk7DL58HRKWaI7veKdUpixTN4e1MmuhsYyqSzplKACN50Dyyi8nOCZ2qazzZ3dQSkL5OXPd0bdAH5xi7QytrxdRpV2SEuoyJCJRaI+KdDY6vJgt1QUdYnPsmShUq3n8bm6NLrzakCbSkl1l0w5Yr1seH2cYg1lhFb9pYtHXZ69atUcM2O7VE3qIYLpqZ9eNp/38UpisQLYsIDDHmONZ40rmTUQXfKQhP/nMgW+4VSBOWO7FAAnnyVTq5J5liXSIYnmDgn9cG8pvOZF8Y5NknemKrkbmoXBLzGdFHy3pVqK/REpD3RL0BvVG1C9ABW4tUWC0hnld8CjmRvc38Vst4zjK210FdFJg5ZQFn3p+7jH536/s+lydx3P8mntru58ONclnhbLIjG0sBGT0scpuMhedtllJtaUHK65CCzQWJbvvvvufsVhB7788stN/CgnsTZmEqy+WHMBzRA3WYHoCUIt4l4vueQSc/inP/2psQRj+SWG2AruwU4W6CuvvNKkZrrvvvuM9ReX7nQWaABlehokwB8pj7AIO1mgc+1LLmmQiHcm7hnL6rnnnmuGALjHOmrTIGH9BswjHAc4WuspFnMmDdIZpbEmI7SFUB8XcOe4Ob5gwQLjhs1YaQsQzb1hPx9yIc+dO5eipi3KIMRyp7NAY1W318u1X6Yx/QdBmZWFCxfazWG99lSONykVJKwvx5qHwlMGiEh22b7HmY8t+dFJvENjLzuT8Zfe2qnDemwfducKdWIq3LBDmjtKdBY5KqWF3caCST9Us/qXMOsuzSfYocA3FldbsVrQi+smf9hdHbbX81eP0gmW5PfddhLG7BqdVGAZUHR2xl+VZNEfsFwenPSUjdX33uQXuaiiW6bOWyctm0ulZUuZdLfpvadg2OuLS0FxVCrGtinwbRVfQc/EQ4F6hFTmaDXOA13aIU7af/KgDNBefTne+/Dptoq7dmggtu4lCd/d+4y0p749r0nqKqNSXxmRGaN6ALA9qeuu28+WomteEE+R6ynjUIuMPXCavPvIc3pISbCiAbM4z/duq2fHvvW9u+5WHw2U10+QxiW5hUjGozr5OiXJEdOnEXdnt2ogLwEwFmCsorgaA7SwECLk1eW4dbHFSoyr8VAEYG0FUIQVGTdk0vDMnDnTWDc5j6tzNhZoUghlSoMEO7OVL33pS2YzPQ2SPW9dn9PTINnzzjWz0840SDBQA0zpP3HJgFArWDGRwfoyUBokYn+xkgLiX375ZePejMUVsamE6urqzKw5btgAXqzmr732mrHq8vkhsC3D+Ew/0xmlOW/b4jMA/B500EGmDdrGRRygapmbcaXGaoz1GcstVlnaZBLAtsUEBYLruZMF+jvf+Y6xZNvr5dov09ge+s+jL7od7V4p1TmBREQh2k7VjVqDPAFdsK7pZGdCY2EkqufMpL1uq4QVxHUXHyDBPXTcH0S3O8rUypZYKj51JWUWuamDn2WNF9R9Fmbr0+Oto5rqR8bXfRDd2SPb9OpvhIyvF3l36ZD775k8I+U+PeTKI6yCZyy/rWoN6hFyhFZNVKCry6Ci7uXeqccPWizfClSOr5bZJ+wvb/z1FbWk93opOPVACqRjr+h9zjrP5ft25LFvZFXBBfsPcF927pTIC/8tgfn/mbV+vp3A22XTIwtyHvaWJ5+XxBVn6jPdtV6mK236eSfJqz/cJJF2SAOzC7obf9QcnTR0Pd+ya2n3nMnbuxoAiSWPuFnLBLx27VoDZA455BBZoNbBa665ZsifCtZYuwC4hsoCDRBDAKGITT2E+zPniAHGBZc4YFx66bsFpaaC/gPQArAB89YVGvCWSxokrJ64FWPNBAwef3zvC81Q+oILNbHDzgVgyMIEA5bW559/PgVAsXoTT2vTIAHImTCAHOzWW2+V6667zrhzE9drcx4Tt8yCtd66TX/xi180Exrox7aFdRuBGAur/2233WYAMNtW3+gMIM3EAwCbfjI5Qju2LZvHmXNOFujPfvazRs+WwTvXfplO7aH/Gha/K28srVdAm5wUMMNQwJsIqUW4wyfxNs0J3KnALQzwTYJfynR2FsgLf95girv/1LNAZ4ZfXLDREGj01UcydjWq8UXp4BdX6BbNEfzifcm48b718nfvlR3jpFtJSIYiYS2/qHHcUKqM7LL6VY3PqJWEr/c7m8uA9ZaUeLUSspWX5FI878pc/N//pmTvBfpd7jt09lku+ePVMm6mayHqqx29p7a/o8vK9MO57UdCEtV4YFd6NbDlX69L+7pNUujBWybtZuwtZs4VqVt0+5oNsvWF5Lton9Pujkw/5wQprxvPC/eA2mDSYe51lw1Yxj25ezQwtLeF3dPHD+yqMCoDwnCjBdwR+4mlEKvk0Ucf/b5dNxMLdLbGrRttfX29KQK4BZzNmTPHuDafffbZcvHFFxvgS0qeTGmQALtOFuhc0yBZIixYmCF2Yv2HP/wh1dWh9AVA6kyBxDb9ZcIBEEu/aRuQC0jHkg24Z6zEw5L72FpcbQfoHwK4RyjrXLNNW3ax5+0EAXrDXRqgmy4AdgSXbyvcC0wwoD/aot+IbdeWs5+Z3bfn7Zrjtk+sncdtnT1tvfbpRfLOyirZtr1cJ0sGfgA4x/bMv2bLpheXqUtgX3dVZ5l82t761joNnRBZ2lRjCEgGGzsvy8QPvrWzRlb+801zXw1WJx/ORyNRefW1VlndUpZhMiGzBmDt3Nheor/3jYO6qGZuYeQdjXevkfh0dYMuCgz4epw+cn4B4gfVSbTDnZRJ1w37m1dslsaWbgnro4cJLL7HLFFdQnof/uvuhZmq5f2x2Mq/q5KSz91dUkY8IvHNLoCzulv712cl3NKu5FdxKSbu13zL9SZMCUE3CSlSgBxQ7w/KrtE6rvTXgC9YIKc+cJP5Iht+jrQiHFOfGDnzmd9I6fjatLPu7nDQAL52eS8ALVL+fFCSiQUaEHb77bebSwKKcAGGSIk1fUlPg4S1Op0F+pFHHjHEWsQNQ8g0EAu0dYV+9dVXMwIwC34tCzSxr+RDxg2YvLnk4H3qqafM9lD74tQr8ba4lROnDOEV1yVeGMHiikAchZDmCBIqLLL0G8st4NcCWOpiscZVndRRpK2iDICUBcCKqzUTGtSDaRlSLY6PGzdOjjnmGAO6sdRjVcclG5IqXK5Jj0RbgFVc5RHLAt3Q0GDSIDEZwcQJVmoLyimXS79sHDPlYba25FfEY+8JLNBNqzYad76/P7ufnHXqK1Ja0qWWcueDlJElhfmGcMQvf3tmf2luKZFAqepoZ5sU11baInm7btqw3ViBd2qqhFe318qho7fr/c0kT3+VANjCahF+dftofXHWWMxwVOMyQ8ra66ZCatnarG56HnlqwySpDK6S0UUhJbrqr0N7BEsx4PfRtXVSWJaQth1tUjGmwp7O23Ui2iBS4JXY8dPE/9Bb5vUYcDuQYC2OzZ+m1l/1Wggnf7sHKp+P55Yu0BAHJR8KR2IGBKfrYPn/Lk8/5O6rBhKa9kiiyYnvXVKI8gIk2rbtUtWRWGnnstWpYRUowC3zRMyzJNaT0pAwHD+hDI4vfdPSVak67kZfDfiLgrLNP0r8nS1SpgSWftUfEld9dsR8EimrklBHRJJ+nX3runu7XwN5DYBXrVplLH6ATusGnf6RAI4AgIOJtQLmwgINgAP0/v73v+/XLADxRz/6UZ80SFijiUvNxAINyzNi0yDRD2ttTGeBtmmQbF+pZ/uSiQUaIi4sycS4QuKFnobSF9rPJIwPl2abBgkXY1iZAbCIZVs+7LDDUmmQAKy4Y6ezLWNdx/XYtgWrN27nxO4CsAHOdrzEC1sBZENYhjs2nzGCjgH9AFIr5513niEfY9/266yzzkqlQSLul5zRTEDY2HHKDdYvJws0XggsCN4Ie4KEO5IvJbjn3v/oIbLPjE1ywD7rTfyvV9lhMc7HFGQQQ7hla6W8/PoUaW1LAjWvXx8Mne/hpWZPUFCOfYyEwub7SvHWSKE8v3Wc1JW1SG1hl+oQbwYYZBNq1fTKhvZS2dRZamKCKe8r8Em4s9sFwKqLsOoRIiH1z5D73p0qB9U2yEGjNee4QriAWjt4oWNGHpdnvTvllW218saO5O+NT+9H9OiKAo44elBFFWlqqLP3E++z+sLcqt/VcEy11lcSCpRFrSCxI+pEky8nT8Y7+hZy94wGtjz3uhKIYXFL12JSQeH2Tom0tUtBmc4OupLSQKK7PbW9qxsJdYV2JamBWJch5Eipg9/FgALegSTW5f42DqifWFzTRgWkXZekAIKT3/MiJRSMhFz9DaS/3XkubwEwcaPElgIWB5Lvfve7OQFg666bCwu001rovDZtvPnmmwaIAT5tGiTAXDYWaFIgOdMg0QaA7y9/+YuJU01ngcad11pYuba1qGZjgaYtwO9pp51mllz7ko0FGostMbSATazAAHiAtXVBpk+WbRnAf/311wsW15tvvlkg1iJm1wJay7ZcWKjso2oyA8wTx22t+ZQ7/fTTzfUA8daFmWtYsRMf1ANE233OY1XGYsyaOF/bL9olXhmwisWW/nEfDaVf9vqsf/zjH6d230/X+1SjH8BG+cQkeEg27ZGlb0+UZSvHy6jqdtVNyKSc6eoqkC0NlYb4ytmFmPr8Fte6c6LopLS2wliGrH5CSoK1vLlGlisICWpewQJ9OcHqy5IuvFCXjCpPP5yX+2Wqh5ha16wsUiv54h2jZIKmQhpVGNKcyRFpjxTIdk2LtEnZtiEWs4L7dPloV4/ow+PHK6PnXtMUKHG1BHsa2sWzXr10tisY6VYQF9C49IpCSUyq0kXLp8xFPk2xMtaq1V33aODdOx6UTmX3L/CUS6TH0paunGK1xP11zhnysef/LEVjldHcFaMBT9VkXQMmBn5Py6ouZcv3lLj6tPopHlsjras22N2c1sWu++6Aeqraa7Q0rHDqtHeSq6tFnz/T3dj+ARW4G0/mJQC+88475Xe/+50hS8KaV1dXl0pRlP5ZEAs7FHFZoHcakqpsLNDEBiNYSS0ItpMHVs8AZARgDLB94YUXjIWVY6QqysS2jBsy4Bi3ZcA/Ysvx+QJ+SWk1depUcw6wCus35FvIAw88YMAs4BkGbED1PffcYyzJtA05lu3XUFigB+qXufAe+m+veXNkxT2PSbir98c+oaBie2O5WQYaVsXECmVEDA5UJG/OTT50uqa2xDKULhpzrmB4oLnjsfvWCdZLV/S7Xl0qoybXysa31qfUgZv4urYys6QOZtgYN22cBEtchk5U4yucpf8d96O6ciTGlJklg+r6HvIWia94Tt9jeb639v6nZPH//bWM0Z+7Ql+JRJQV31qHnKrZp0LZjFXX/3vBV+X4R38rvqC1JjlL5d+2b/JciRbq5FRXy64NPqqeIRPce9Iqb9whM6ThhdfUG6Z3AtCey7TGg4s6rmTXwPHfOlfuPr/XiOEs+ZHzjpWSGndy1amT4bSdlwCYOE/kiSeeeF/JrmjTmZOXfQTghIsxsbO41CLElhLn6xQsvqTiwTIKC3R9fX0fFuhnn33WxKYC7CCNwnqKGzUkTxdccEGqKdoGOMICDcjEFZpcs0NhgQYY4sabjQV6sL5g0cVt2SkWQOKWjGUXkimIsdIts5ZtmT5AxHXLLbfIPvvsY1IeYaG1qYuItWWxLNBcCxZo9EFOZmKmAdp8zsg555xj8gizzXnc1S1zM58Ncu2115px2+1nnnnGfB5YiEnFhDAOWKARXNaxDjPxYdvKpV+m8h78b+LeHeqmz4tywZBGURCIyuw5W4ZUZyQXDijwmnHCHFny0ItDGqbP75UjLjt5SHVGeuFjLz1e/vz1P6nLmbKK5SgFSvZ03BUn5lh65BfzeIPiLzlCIi3PaDjDwK6R6drw+MrEWzgz/XDe7sfVs2DpT5JhTpBqH1PbKI9sGWtiLIm5JN4S2a+iVcYWqmuq7nY1NMqGR56Ruk+632104607nFgPNocuvAdpfU+gZOh1R2iNwq2L1WEjYcJBchkiASPBzW9o0QtzKZ6XZSYXr5TTDl0ij7y8nxSqpxHfakKXZk7aIcdU3C+J6CXqWbOL93BeavTDG3Ru00AfXn8+lCutX7/egLMPy93UZYE+1rhzA3YRAClEWLBA416cLhBEEZdLKirSKJ1wwgly+OH6IFQB2JOmCrFsynbNMRv/zJrjuCnffvvtJpURcbdYfnEnJ5YY92U7CWFd4SG/cooFtUw6WKDuvB5lreuzrWfP2zXH0/tly+6pa+/mv8tBR6yRQDB3sMEbXlFxWKbXv665g91YQfvZ7zt/314vUntwwHVCUy7HZPpx+w9YKt9OHvnZoyTsT/Ir5DL2eCIuEX9M5p5zRC7F86ZMsPZzEuka+qtBoOZy/c0der2RqtjG15aKM35S56zkE+O3ytzqZgW9bbK/LvM1Tr1OCQStRDUWeM29j9vdvF97/EEpOPo/JO7viTEfikaUbTFw4reHUmNEl42rF1zotRdlUnWTjjM3l/L62h0SWrRQye36xg6PaEUNYXDR9W9J5x++JFPGNMkVJz8vJx+4Qk6c87acf8wiOXbflRLbukq6X3xgCC26RT9MDeTl0wpSK+I2t237cNgBB2KBBpwBBH/xi18Y6zBuv4OxQGMZBkBaFuitW7emXITtzXPmmWea2OX//u//FgA/LNAHH3xwP7Bmy2O1xEoNCzSgEzdg+gEL9GuvvWYspplYoHPpi72GXWNlxTqdbiG251njckxaKuKVsZpv2JCMsYB5mWsi9NmyQL/11lvm87zxxhvNGAGlAFastViUsRwTy0xMMIAaEi7yCWMVRqxVmTRJy5YtM9fDTd4yP2OtttuWBRrSK8ivBmKBztYvc9Gef5CxYRVnAZzvCZLYtkj2PXCTTJmedCEfvM88cD1y9kWLNB+mxhA2rxq8Sp6UCG3eJrWB3pfggYed1OOk0V5pWrFu4KJ5dragMCAvKQM0E0+A24EkBviVmLxUulrvx7x0hMqqHk/BRFlws8b25igRvXXfvL9cmjclf5dzrDbiizUvfUfTQvUlYCJcerRae/cu7ZTJJSEp1gmbdGl9x/1eO3XiP/LfpTOxlz7XVXk5CgSMHVMvc92fHfrqWqu/jUo4WlbYLVM12wAgWH8pHSWSmxzzeWNSV7NDufCi4ikokO61q/uVcw8oN+Brf1MPheTzI1gQk8mjm2TK2J1SVtSdVE93h3Q/c7urqmGqgbwEwIAoXpIAd++XWCsgbrV2IcYU8iUALnlvTznlFHM5y7yM+zKLjVsF5OFSSz3iYnHLtczLuDyfeOKJsmbNGgMMP/axjwluuXV1daZNWKAR+mGtjbg+0w5gk2vCAs2+tXZS3vYlnQUaYAkRF+UZw9/+9rch94X2MwkEUgjxvLgiZxJicPmMAIQAYFyRkUsuuSRV3LIto1sIrIjxRT+wQCOwQDsFYIs+GTOAGIBsWZct0/fGjRsNUOaaEJphcUboK9dDLAs0kwxMFODujbt1Ogt0rv266aabjBs1rtS4v+8JkggldXvMx96Wjxy2VgnCcIfu/zBlLLg9V1SF5LzLF0qwMKpMs1quM1fgvCdo4731sXX5CpOXcaySNfmU9Arm4v6SzM8YULfUCUWdIu0t0qnhAa701cC27Q3yYOwlaZR2CSfUHS2N5DCu+916fIe0yt/ib0jDlgY3J3VfFUpUGbXX/lPk3ksqJdTiyZqFJh5TTqw2j7x6R7G89CuftG/6cCaU07o7bHdD6s6c0OfxkEV/H52W4yHXH2EVPPoMXt5xjnS0F+pze/BX1nDYJytX1Ulr+bEjTBPvbTjRZn236JkYLA5EZObYbVKjJIEFGsrk4bmjC9scm6YAudyCOP2iR5t2vreLj9Da8caN+tLNu092SbS67zrZtbN7z+Tl1Dd5ZYmHBSCSGufII480saGAvXSBNMkSJ6Wfc+7bui4L9HNGLQOxQGNBhfnYpigC6AJwj9NcvAiuyT/4wQ9MfLa1ilqgjEWV9EhILizQlAP4kwuYSQSujTBxgOUVoqurrrrK3AcctzmJ2XYyQmOJtqmLmGR4P1mgv/Wtb5nYY67pBPjsD1cJN0XE0rTMPXqNzNh3qyxaWCcbVler3pQNVoFaXPPWVo/qkFkHbJZp+zSoG3oSIMd0IsETK07VH65j/DD6FVm7TLxvPamXGiMBfbcbp+mPOjV/YEfUr/kZsWRqHJvGbAVVnyWaZ7BQ1/p1MUv3QzdL/PRjNO5yF9wDP4zBfcjXeODzP5GgkrvotJosiC+VWimXiZ4aqZZSUUdKJRSLSGOiTTYmGhUAK+mQSkUiII9e/Ss5/ZZrPuTeDt/LbfnVj/VFOSaNq/xy78VVsv85nTJtflgCJRo7qO96hGSyXv9ygSz6U7E0rfNr6uCY7Pjvn0ndsXcqE7QWcEWKRo9Si5tOOEeHBoIBfF6XBKvPHVQ8bow8+qtDZPbs9TJr1kaTbi8Q6NUrc6oRzTUfDvvltUVTZEdHvRxX3T+8qk+j+bajQC3W1qqj1geISkDDP8ZXtprFHMjyL6bvYwm+8K7004C3dnLyBzGW2ZBDBW/VuH713APDQwN5CYCxaP785z83n8Add9whLNkEV1lSIeUqLgv04CzQP/nJTwz7MhZ43IxfeeUVQc9YTInJhbgLiysxwFhmTz75ZHn66adN3C4uzkxezJ4927g3M4GBRXcgtmXijGF5/vd//3eTyomUT9/+9reNdXvlypXmo/30pz9t4oyxumMFBuDOnDlTIEzDUozLObHJyPvNAm3JtWgby/twl7BOIjSv7JRRU4nJTva2siYk8z++3OxEdAaepbA4ouf7WzOZad7y1+dk8rXJuO7hPt4Psn8tt35Hasp1Fn5bTCLK+oyLZKm+mLAMJADjisRm6XzyT1L6icsHKpoX51b/8w1Z++xi2ctXKkujSS+K7Wrl3Z7QF77+t2BKJ3XeMnn3qVdlw4vLZNJhs1PH83Wjc+UKaf7736QiWCaNoWLp3OmVF39bqoum/C2LS3G15rxs8UqomZfo5Iu01VWwab3sfPQhGXVmMqzEHs/XddX+M8RfXCSRVk0fNQSpmLW3TnD11e0Qqo/IouMO20+W//FxWbZ0L1n59gQZM6ZZxoxrkpLibolpjvT2tkINUaqRxh14l3k0n3JMaudMH5G62NVBNT90965VVasxdcsPn7dr9UdwrcAB86XrYZ0wzCoJKdjP1VtW9ezmE3kJgI844ggTP5qL7ombHYq4LNBJRupsLNDPP/+8LNSciLj9khIJwIf7MCRTpC8CAOPGTMwtQvwxixXcjH/zm98Y1uVc2JZxW8aaPGXKlD7MzZ///OdNHwDPCJ8zC+7NVu666y5jBYa8C4uxjc/NdxboLXffI21ri6V6MpaKpP6szlgX6Mw8SybBA6ujoVC2v/S4TLzsKk2bkr/Wy/DK1yS6YaWMKlNGbbXsOtLYZlJd6hgu0mMrW8Qb7pCOh5TJ/FSXZXLBD+6U7tZOOThQK+9GW9Ta2/++TCmwZwOIcVBglKn3zPf+JBc9cUN6kbzb33LLzyXe0S6TK2PS0h2UqCP/dHebV12e+7ugEjM4sbxVEqFO2Xrrf0nNaWe4rKd651TPmSX+0uIhAWDK1597at7dd4MNePwR+0uhWnTDLe3Gwwiwy5JJvGp1n/Lxj0pAdelKUgNd7yyXrmVOBuhcJ1gSxgOpa+kb0vXuCimc6rK823sqoSm2InecoN/xqERbMxkuNGzJr7OvS2+R+FHnqCW4zlZ118NEA3kJgCE+suRHH8bn8H6xQBPnS/wy5FG4DAPMsJpikSR+1Mar4o59xRVXyM9+9jP5n//5H8G6ifUTy6t11c40bkuEReoh8h8/99xzJn75C1/4gilOeiFmpufMmSOD9QVwa3P+Oq+FS3NRUVGKydmeAzATZ4x8//vfN+PENRnrL3LvvfcaK+/DDz9scvRyzM6S2zXHbPwza47j8oxFt76+ntMpIcYYoS8I7tHoB4v0mDFjDAi/9dZbjdUX12zaclmgjaqk6Z//lO6tBRJqCkrJ6FDKCpw8O/D/uBKZbHlD8zyrPts05Velhh/kq3S/+oyChjZjJd+/bpO8/E6dqmLwFxOigWfvtcWojRjDyLuLJTBzaBN1I0nnHdtbZOfqpD7Ga67VjxSMklci25XiKrvp16c6/GhgrIzxJV+St69Yr1bNdimqLB1JqhnSWOLdXdLx1pumTm1xSGqKQtLQUaxa7A96extOmPN7VyWt7rwUdq5YLiX77t9bJE+3AGL7f+MKeek/dGIlC9eFUzVxvV0LR9fIxJPnOQ+726oBdHnsL78qj5z5VSG9VFbR50pcXc4Pu+6yrEXy8UT7wmcl3tUpfl9cwhqilLt4tE5M64akfeECFwA7FBdb9lduTCkoD4lH9RppURCsnlnmEa7fZV9xVAoq1TW6SwHygp9I4MxfOWq7m8NBAwM92YZD/z7QPsACfc0115gYYIAkTMzIlVdemWIdfj864LJA92oRMFpVVdUPiGNVxVoLyLRkU+T+ffTRR+VrX/uaAfm04vf7DfBnOxcWaMogWJWJz+Yzf/PNN1Nu7xakE1fM5AD3AGVxyWYf92mAOPJBsUCbxveQf7x8hDU/M7/y6/41VtMjeHXSIbfOG/C7uEY6GwuFOODOd9/NreIILRVeuQjWOjO6cdWtMmdKkuk8+3CTJCUnzlkmRYZ0TJ+34S6Jrn87e5U8ONOwdK3GpfbO5R5bOEEOC4zWGPPMj7egHj88MEaOCI5NacenTNDbl61L7efjRte6tYYl1o79gDHbpFIZY7HwZhII20oLwjK/frV6MCR/BEyqlXfz+3506mriKfPk3YJxzkMZt2Oq4ojGr4/7xpfFG8hkTcpYLa8O1h4wTc76exJE+DLESBeUFUvltElywZI/S0FxcoI7rxQ0wGA7F78CqQnzzkp0ldk7q391dd9V3gnqSDQinYtf7V8kj4/Elj4IC6DRgL8kJoXjuiQ4WpfaLt0OSaAqktSdxk/HVjyWx5oavkPP/IYwfPv7vvUMCyY5X4nZfeGFF0xMKuRGLFgBAV+//a0GPg1RaNcuECxdf/31/VigaRIL5a6wQAMAkWws0Oak/sNimY0F2paxa/qSiQWa2GdclEmLlIkFOte+2OuwBtxWVFQ4D5ltmw8Y5ubtBmCJ0T86hBnb1rn66quNW7JtC+u6k20ZZmwnC7QF0+QOxnUapmjaqK2tNUC8paXFXB9CLVivIeC66KKLZPXq1TJ27FjjPm1zAdu2nCzQ6CYXFuj0fjkVwCQMVnUWS9LlPD+ctiPqgu7tiYWOdBbI8ofrzI88jLADSVSBcsPySgUZ1cli6nrevWnzQFVG/LnYzq19xji5tkkOn7FKSpUIi1l3vxILeXuYOdkfV9UqR+/7jhQ5cy9HuiW6I7/12L6tqZ9VaF5wvFxQPF0O8NdIpScgheIz6zkFNXK+Hj8q2BeUJKJxadua30yn0cYdej/2zmbx4nvohM2y/5gGKdc0XQBeyK5YFyvwnVrdKIdN3KT3aW+dhLL2hzUtnyu9Gnh1R5E801AmIfV+CafNJWD17cIrprtAHmiaIA2bk8+j3trullMDlXtPlPMW/Unqzj1JtkfiElIFdsQSskmfP4d991I58/FfSDCPvTicunJuRxt7mYjh7TDA1nzX+e72fn+T28mMAwXKQwEnhZVoY4PddNeqgUTjmj564PfSW6D+Mrr0S4ce7ZZEpG9KtD6V3Z3dooHeafPdcvndc1Hy4l588cUCsAH0zJs3z4AYeoPV75ZbbpEvf/nLJrUO8cL77bffoB2tr683aXduvvnmVFnaAmjhxosbMS7LCKRHWEKdbtjEomJh/Mc//mFIoOjXsccea9yQH3zwQRN/ShokcuMC0jlHuiTclFms1NXVmXZoD1dprNn06b777jO5dQFruCFboS+0CTEUkwH333+/icW1MbEcA0zPmjXLEEjl2pdsLNAATcuuDNEV6YbQHZZehLHZ+F9AI5MExP2edtpp8rq6zOKmTNkzzjjDsEDTFqRZpDfimgBawKtti+shn/rUp8xnunz5ciFOm9zA5EbmelYOPfRQOeigg8zEAToinZVN2UQZ29bhhx8uX/ziF00OYMixsF4727JjpF+wR/O5QublHKO9JmsmM5hwQcjbPJzFV6IukapzK9Fuvyy5d4qMPaBRqqe0GWuwr0Df8vRhEI/qP3UJinT6ZdNro6R1Y6mtZta+cghL8le8RaXqpttXRle2y/zKt6UtFJSWTiXQUXe1gM7CQ5RVqDkZ+4lXGbdL8pvtNFBapC8ceq+lyWhfkXysaK+0o1l2tX5QLUj5LF6eT8534R5ljNW0KCwxZXUPK0M5FiQn6O2jM3279vWkuetzPI93gsUBWbG1WFZ3Fkq9kjaxlCi4CCt503b9/VzTGZRtCoBLqwqksCSYx5rKbejFo6tk7tcvlAtv+LOpQOhXeWWJfPfTJ+TWQB6W8hb1/W3j5xKAC5ki2RowgiAGxOmEFpkH2HaKt6jEuetuB/rqdECFQH7ic7/bA+poN5zMSwCMZRfA9NRTTwmgB7dYp1x66aXGOgzIhCEa0DWY4DabLoCrrTobDlMxABPQhQB+sTSSH9gpAHJSND3++OMGrAIMEWJvERuLa3Z6/hG7unNnknmZQ4BGcslCKAXoA5gtWLDAWLUB8++q26kFmJSnLwBc4oUBuVhUcRGGaRn3YHLnYh2GdRnJtS+MAQDplLuUVAqwuHjxYvnKV76SSoNEHC59QyDDogwC+MRia4XjTCoAYgHA7JMWCUF35OQFbJ566qnmGG1RHrn99ttTqY4YLxMB3AOUsQLohTDL5g8mt/Dll19u4qEpY/vFJAX3hL1vmCxwtkW5wcZor8n6pJNOSu2io+EsPtUz4NYpgOCNL48xsb2lYzqVfCSqLqkamxryK+FVkbLG9v/h96gVOTh6jLOZvNv2jhovovG7maRMczCyDCaeYJF4K2sHKzaiz5eMrtRZ9/f2KOMlmnbyWQpqkr+72XSAm3ORN8MkjKMCILpgVH7fjw51mM19Pzpdtq5pMID37fYiYckkcfWDnnFw8pmf6bx7rFcDxSVF8vK6++SY2RfJSZ84Um763Vd7T7pb/TRQMHa8dC1PxvfbkwBcnwJdG75gj2dbF4ybmO1UXh731s+T2CYlaFUX58HEUztdJ2nz1uF2MPXstvPv7a1ht3X7vV34jTfeMGltAL/ZBKsw1lsA41DECfpgDcYCiFWPtDtYM23MKS9cv/71r03T5LjFeglQhdwKAUABgLn+22+/LWeeeaZJDQRIw/W4rq7OgHes1YDSCy64wNSz/7gmlspJkyYZi+aFF15oQK2d6bPlWNMXwC8C6CTmFoAKcP/GN74hp5xyijk3lL4AMLFAOwVgyIKbL6CXNEjEA992220GsGMhhxHasi0DZnFXPv744w2wZ0xYgy0ZFW0B/gGfxO6S63fChAnGhZpx0Ja1ugPkcQnnc8WSDNDFeox+ED4n8gXbSYof/ehHJj0ToBodT58+PXWOiRPigrFKM7FBHl9nW7mM0amXPWnboxMKZQfMkRYNG0iXmKY+atmQm1XXq/dwxeHJfM7p7eTLftFhH5PuV5T4TV34dlnUtSo4Z94uVx8JFSccNE3zVOoM+3sRfRkcd8De76WFPb5ucOJe4tMJwWSu0F0cjoK48rnJycxdbGHEVTvz6pNkwT0LlQcr3d+j71BnKfitnZiZ2bhvSXcPDYybUCtvtzzuKiMHDZQdc7J0LHxayayy56sdqBlvYUDKjz5xoCJ5d87/kfMk9vLvNIZhkLAFj0/8h/9b3ulnTxhwXk5JADhtTtdsHxKgEKBpWYKzlUs/Thofu+D2SjwqIBIyJnLMWsEKacvhkou1GbBl402d1l/Knn/++QaQAuCwcC5ZssSsLQu0TedD+5Sn/zfccIPJkUssKyzQgEBrtbT9SF/TT6yQtIEAQq1YFuhc+gLQZ0zOBTAKMKWvxymzMmXYB0zSX7a5ro33xTqLm3h1dbVxIZ4/f77pigXxWI1DoZCce+65QnolwCznsIozuUFbWIBxSaYt9MY10CGuywhxtwgWc4A3ceHEPUOKde2115pJkB//+MemDPcMY2CNdZ17g/hkO5EC+EZyGaMpuIf+q1XgL773NneGlaiwZ/JhD1XDe+52YNosvUffC3DT2fsSXSoGtty9544O8waYWZ948PT31MvJhyc9XN5TIyOgcvUZ56gXdPK3f6jDYRqncNpM8VdWDbXqiC4fa9Sctf6BgEdyAmxcvMM8G0e0MtzB7RYNlB76UQ1vGOgeHKxbYSk55MjBCuXVee+oqdJQ82mJagx/NonpuYbCY8Q/59xsRdzju1EDeQmAcTPGuokrbTZ59tlnZcuWLQakZiuT6/FMaZAAgaT2scuf/vQn+eY3v2lIoiCEAhwD1LE2AqQ3bNhg+nOkpo0BCGIFffLJJ411FjfrF198MdUdQB9pkADJpEFCcIXGVdcZ85qq0LMB+MXdGbdiyJ323ntvY6UlD++u9iX9GlhwAaaAaa4DIAdgAoCx5CKAWFI3sU/8MiAc6zjxyYi1LFtyLHRBzHRdXZ2xpAOKAfwIkxhMLBBvjOs710dX6AagyxgRXNix+OOaDjC3rtOAZSzw1Kc/uI+jJ2dbkKghdrIglzGaCnvov6pjj9EXNU1/souGS16wSw49fA8d/fvY7R1vSHCsKlFzAO+KeHzqkjpRyTU6tu9K9RFVJ0i+xUwBrDmMkrzKAVM/h8IjvEhwxn67DIBRf2BGckJxhKtpSMN76U/PSG0wIXXFGjuNy6kuyp2vtGzJdbnee/tVxGTnmq2yc13DkNp2C7sayEUDnvA2qdq3W0NFduFZo/crdWnDlb4aeOS+gDz89P4S6vJLONILpwC+HFuycrz84beV0t2ezEbSt7a7t7s18N7MOLu797t4fSyYkDthEcTVF3Ipp0BWdNlll5l8s84YVGeZoWxnSoMEyIMkyQrWRCyVAEEIuABgWDIBXcSI0idcerE2AuqwYJL/98YbbzRxyjBP2zha2sRlGhBvXaEBdbj4fuYzn8k4y2zBL+7hWI5xYQa4E3cMGP785z+/y32xY2RNzDFWa3LtEmOLEF8MqAfQA2bRBVbhmTNnGtdmcv+iF+uejHXWCjokHhc3ZMSyVgNmrTAWPmdczu+8806jZyYCLrnkkhTQJS8wwHmtxlBDhGWFSRAEUIuOEFzCAeO0hbWXnMlMVOCOjeQyRqcHAmDaxjLjCo9uhrMk1M0xlCiSYmkfcjcBzZGYV+NWXVc/gGvR6HaJNhVJtINZ5Owzyf0UrS8lRZpqoaC8xABgT0nv/d6vbB4c6NrZKoU6kdClxEJD0qOWLlJSp67GQdzY8kCHDDHW0SUhqZDiRJNOVOY+aL7XnV0BKRvAGpJ7ayOr5KbFa82AqgIiFQpAOtUTuluJhwDAxfoGFuh5b2ZScfOSdTKqfuzIUsD7PBpipVe/+LYs//sbcs337pAKjQe++frPy+yTPiJjZyS9sN7nS+7xzSU6t0vFTP2d26Yko5sLNWSkF6wNNDiPxv2XTWmXihl6t4bUeFFRP1DxvDu3/Z3NsrF9gqzZMEpmTtkqk8Y1GXKxhsYyWb56rDQ0lktxtU8aVm6SSQfmd4jNcLw58hIAYwEG5AE0sRRaN2dYlTkO2EF+/vOfp1xlB/vwcFkGrDpjgIkLJaUPLM2APhtLa5mXnW0CvlhIwYN7MIL7MHWxPgLyIH7CUgl4gzkY4iosqJlYoAHYxLzSHmMC/OEKDRhMZ4HGgpyeBoly48ePNxMFWGwtC/RQ+uIcn90mlRAuzekMyViEAcAQUHFdhNhfAD8xu+iW8XLMui1TBqsvbXEe4i5icul7umABB9ATU4yrM+D67LPPThXD4o6LOvHPWJbxDmDCYeHChaYM/bYAmM+HyQQsyZYF+qWXXkrdN0MZI41THuCLOF3ZzYHh+I+3XW+BdITUtTwYNpbgXF6WqRbVB29XtMBY+Yfj0D7sPqG3smnt0vxWhSQIEVRWzsHE44tLoCYshaPDivUgcVPF5ruoCmAmLvLENN0MjzV0MpAukzorVF0aAmluTleMBjTZkbR1FEp5adeg323Uhua6lMU4ElW9u2rsfxc57i3utVJVU2kWRfHcdiW7BrYp4PjjJb+Upg07pKM5OQHboZM2j//gPvn7Tx6UaUfvKxfddnWfvODZW8unM3pf6b035rAmaXi5Sjo2KQjWieiBBGtxycSQjJqjnnkeTV/o3ptZ1dWhWRsWLZ1sln6F9CGfKUtBv3LugQ9dA3kJgNHyl770JZk7d64BPK+88opRPNY/rKvkjL3pppvk6KOPzvkDAZjiJnz33Xf3q0P8KWzClpAJC2EmweoL6zSgGZdeK7AOA9pwYcZqifz0pz81fcXySwyxFcbgZIFOT4OUiQUat9/0NEiAQFIeYRF2skDn2hcAnbWI2r4Ro4uVG6CfzgJt43utizZAkAkJLK32pQArLa7RmYTYaVyVLZDMVGbq1KnGmk1+Y/r22c9+1uQGZmIASz+6tPG+tr5NWYXF1oJTgHI6CzTnbN9zHaO9BveilaHcc7bOh732BgMCGVYs4ZX27oAUFWjCd32hM0AiQ2fsc7NbU/qEYwViGKCr3ThBT7FawQuKxZNok8r9WqRzY5GEdwZ0dl7fVDIBYbVwKp+GFE/olGB1TzyXJmD2FOd3DDC3XOnYasFBz6eqK/ZF1cLmdXCL6cGUJAEGbqhB1ae9Z0vHuR4JqKigqiL13W5tL5RCzTlNuhTEOcnFdxpNkkIlpL8BMU2P5AkUSHBMfnsiGEWl/Rszc6I0b2pMO9p/Fy+nMTMm9j/hHjEaaNrYKD856v9odgGd+FPhW316efI5Eu2OCMtbj78qX669QG7c+AcJlhaacu4/1VWRPiOSP30y+tAm6dhYKI066RrrUmd8vKKtRbjnGeMLxqRmv1YpmdDjuqtfePc50/9Oqq7X7BfqtTGQxMJRGTXF9eoYSEe761zeAmAUTjwt5EkAF4AjFtspU6YYN9pd/UBwrbYCKMKFFjdkADVWR1yRER522Vigs6VBwjppxYIm3KQBc1hVnWJdn9PTIDnL2G0svM40SDBQEzdL/4lLdqbpwVKLDNaXgdIgEX9N2+ks0LRr0xLZfMR8RlhhAa+M8/rrrzcpjdCfFSyoTAhgPbaxu/acc41VF+AKoMYaz2QDbuKAao4xyUD8NczUgGL6aCdHcHW2BGW4njtZoL/zne8Y4G37Dgt0LmN09m1P2oZ0yD+5TqIawx1XENwRDqj1Te1GJj9oMsaIZy0vKOQZxOobVuuQJdeJ6gOh+ohD9qQhfyB99e4FqUjyrQRwUTIpJEVju6S7KSCRpgKJh3k50dljjfX1FcUkUBmRQFVYZ5Md3SmqUiPw8HaZd/T2A9usP3murFvwukRDGuemuixSy656lCoI1jyXLHpl1EZ+SxODyc3ZI/5izc960ly7m9fr8v335UtrdMB3u7MrqDqL62RrXNOl6ISBLoBeloha2uP2xVlreHVys/qjbmx/+g102OeOl2XPvCk+Zg0GkGBViQJg14U3k4raG1vle/tclelUn2PxqN6jfp/cdv5P5YoH/4/hG+lTIE93PJXqfuvXCYHupAJKJnbpRGqXRNr80rU9qL+byYeKvyguhbXdUlAW7TPhJX7NtV4xJU+1l33YR1x2sjz69f+S7g7nQ7m3vEefNzNPmC2F5cW9B92tYaOBvAbA9lMANFkyJHtsV9dOa6xtA0CEFRXL4XnnnWcOAzpxu3UKLs+4+2LBTU+DhPszYA2WYoAWwBf3ZlIrAUqdaZBoG4CI6zPuwbhC55oGiRhaWJixQuMyjMuxFZsGKZe+4IZsyapsffSAGzELMdZ2MuCLX/yiGQMWdNyOEXQFGGWCAiZt3NXvueceM5mABd3WxZ0bUAw5FRZ2myLJXhPrNgAafS1YsMC4cuMujWUXSy3M2AjWYwC2M+0TbQGUAcfE5QKQEcYBCzSCpRxLMhMfFiDnOkbTwB76b0VDUMbrS3BA3U6ButG4zywpQKdHk697DqTRM9a2SIHsbIlJ8aQ9dPDvU7c9FaqAiski25emWvQWqBvvaI2D12UwiSf8UrDPJwcrlhfn6084VJ75yq/7jBUgDOC1d2Kfk46dhE581h13kONI/m4yuTXqowfK1if+qWpLfncBwk6Cl2zaCZQFpWRKXbbTeXv89aWbpTmckEolu/Ix05VFXt4akk4lyykpK8pSIn8PL/j141KgqXgiXcln8ECaiEdjsv711fLuv5bLtKNcdnerq0jNyeJtuUMTOCQnqbkVA+VRs9gymdYxJXeK135MNITdlTQN7Dtvgaw4okuW/r0/wPUZwjGPnPqVV3QiW0nEvMG02u7u7tZA5mmL3d2rD/H6WA3JvZu+rF+//n3rRSYW6GyNWzfa+vp6UwRwC6Al7hVgTNzqxRdfbKzVAL5MaZAA9E4W6FzTIAHc3meMrQAAQABJREFUAOrr1q0zxE6s//CHP6S6OpS+AFCdKZDYpr+MBbFrtgGYdrHH6QtAFcIy4m1xTSf+mPMAYytPPPGEicOln87j9jygFRIvmJoB0VhrEVy+OVdXV2f2cWFH7JptrgcoByxzXQuubR8pg9jPLLnXOzZnOTs+1s7jts6etA6rC9ryt9ukXYFsj7HI0X0+Xxyik2vHCbOJEWRpa7UsfWpx+qm83I/OvFIimj95VyQaViKdQ67claojrk5QZ9h95mmmN9iQREGJqj9Q1v8FZkjNjKDC1RMLjLV3qEOqHdvjlj/UiiO4PF5E9/6/v8lbHQlpieKRkNDfzN57NKo/oN26LGpLSFsoKs89smgEa2PXh7bo3n/lBH7tFULNHbLovn/ZXXetGti+frLE1KtoqEKdhrV7DbXaiC8fj2yXaPtCOeM7jTL/yiYprYlJYZku5cn1/qd0yJcf36QhTq0SbXt+xOtjTxxgXlmAAXS4yhKDioswAnkRbNDpgiUX66LT1Ta9TK77mVigeTDefvvtpglAEWCPvrDGGp2eBgmLaDoL9COPPJJy5YWQaSAWaOsKzZgyATALfi0LNPHRl156qXFTJg3Tfvvtl0rJNNS+OPXEdbAyQzAGYIWt+bbbbjMgEiAJyMSlefXq1aafEHxhmYWYDOs4/Vi1alWKqZlcvbgnZxNcw7FiP/3006ZtLNsInwms0rBlI5CKYS0nbRSu8EwskJqKzx8LOmLTHDU0NJg0SExGYBW35Uwh/ZfLGJ0s0Fi5bdvcm8NdWrYqYZeihhe318oJ4zdrd7PH/zrHEtP3viVNGqvZXiDbVlLPlY5IvXRuGiWjJzXozHzvi3Eumnn7lb1ljpJg5dWPeBbFdDYo+2ZQvRA0DlCn07RUcqItS/Gew8lpGn+BT0I7W6SoumLg4nlyNrHpbRk/pkk2bCa2fHBdMt01urZFAl3teaKh3Ie5Y0uzhDqS3hxLOpTwTidbRhVonLp6zkRVtc0aXr1D5w34bZSWTlm0YJmcfF5/EsfcrzjySoY79Z2gaej31ga1ArvSq4HW5Rtl+/JJsv+Za/Tdqvf4QFvM1Sx+oF7KDtk4ULG8PBfrfEvHnVTkYee2y6Gfape2HT5RWg6pGB1TV/wetcTbJNa+SAoqjstLPQ3nQduPaDj38X3pG4AHkAO4qa+vT7VpwSCgkzhTZMWKFQZs4V4MCMxFrBUwFxZowC+gl/bTBZfaH/3oRyaWFbddADHESICzTCzQuEEjNg0S/bDWxnQWaJsGyfaVerYvmVigIeLCkozVFBKvofaF9tMFcItFHHdhmwYJKyxuzpBuYZEHpAMiESYtWKy8+eab8s9//jMFgAG/WGkpw2ebSYhXxvpMzDW6QXAjx0XculzjDo37M8ds7mTKkf7J3hf0HTnrrLNSaZBoh1zBTEBY9vBMY4RF2zlGy3RNewBoxoTY+9HsDNN/nTq7jpdBe9wvj2+aKCeM2ywFxAAbd+j+ncZKHFV3ymXNlbK6PTlZ0b6zrX/BPDwSaWuXd9+Ad6BTasarh0MOE/RRnZFf8eJe0tJaK9HWNvEXuy6T3coI6y3wGyboqLrmDw6Ck+DXxANrPeq7ADj5BYy3t2p4SEwmT9wum7ZW6QudukCqG3S6eDQ2mBfp2poWKS1R19SEpkqJaIx6gessaXXVrqDWCzNbj7TpyzFLNmlpdH8X03XTqeDXp9/RVABreoEs+yHVvSu9Gojos6J9e5G8evc0mXPWapMTONukayziUWuxT5Y/OUnCnQX6nGntbcjdSmogzvtg75fZq5NbFWN6951qSsRc/Tn1MVy2+z/VhkvP3sd+AEwAigA/GIABWOlCbO7f/vY3swCAACIQQAH6chFLvgQLtF3uu+8+A2xgL3ayQGezKtMGAA+wi9g0SGzjrmtZoAHlMCrjFoyFlMWyF9MG4/zLX/5iYnBhgSbHLH3BFZrFCbLoCyDYskBjLaUugAzASFv0/bTTTkulW8qlL7AxY8V1LgBVgCYWYMYImRSu3Ry3Mdhc7/TTTzcgM11PgOYJEyaYzxKdIIsWLZJzzjlHPve5zxkACsDF3dkppLnCOm5jdDkH0MaSawUSNCYeLPC2x5mksJMM9B2hj9bqTIwx7XBdjiO2HJ+7HQPtM07EljM7+g9dLFmyxCxYnoe7VIyt1IdjMhYLJui/bxkvSxXcdirLc7e+LIc1FygL2xEFI1tDRfLctrEKfntd16sn1Q73YX4o/QvUVBng9taze8uq18dLpFutmBpzlS7M23CuszUoS56dIjs2VEoirN8nl03bqKpodKUQ+4eQDglgm7Re9l8DjzlPOQBcXEnZimtdVnKjPP3nq07+BkF8tdeERhlV0yZFhUouppNcgF5IsQKBiFRVdOj5HUnwq/UAvi74tVpMrkeNq9Tvc+aX4r4lNV5dgfKkqS5bbLpeSmuVrTiSeXI7vaxzv2y069Hh1EdwdPKZG1YPrNfu21s2L65RcOtTVm1dlA3aLLrNsU1v1sjrf5liADNtBHrqOtvL921PgX5XPerOMajAkD9p0FJugQ9fA3lhAb755psNGzNWzuuuu25QLZMGCVAFaMQ91ebvHbSiFnBZoJOM1AOxQAPIsZIC/HC5BtTaiQYsqrgBP/zwwwaYY8XGwor7M7G8uAoDziGhwuIL8MeiD7szYBVAjCX3d7/7nSEM4zODkZl2Tj75ZAPkyf0MkRXM11hfmTTAQ4DrcvwLX/hC6qNmMgQmb0Ar55ChsEDTPqzSuHkzsYAwRqcwkWHFOTlhjw23deU4BW0xLPRJywYgGHCLdbdMUyIVwQatAKNLAXBLRFOkpFmPAppWpW6qG3PJ51q5/z4S73m527hitGxZVSO1k5pldF2TFKpVza9EGuFujX9vLJLGjRWyQxer9+K6Scq8m8sDmCuNbMF6W1hZqqymSauPJcBi4gAIbIU7FtDrlJIxVW4MsEMhRYccJ91LdBJRJxTQVZnmA2YZTAKzDhysSN6dL68qlfpZE+Stl94ddOzFSn710Y+7OkxXlD/gl7GzJsr6V1eln8q679M6+5zs6tKpoOoj50rjcy9IPNQl0S6/6nO0WYqquiSgWQYQwHBnU9/0UV41INQc4bLkO3XJtq94P/3ff7I6vZxOyar789H9D7tHdrsGcvn0dnsn32sHXnvtNQNyMoHfbIDjhBNOMJeFjXkoQuywXQ466CDj9gogw7JI7KwVrkucLwvlsfaSegiwhNjrOpmXOXfooYfKcccdZ6y/uOcCAm1qImfbWB5x58UyCfgD5P3whz9MuQDbsqwpi4suYlmgAXwQWWVjgR6sL7BAA1idCwCSBYs85FZMLmDJRojNxaqKSzKAleujG+KzOQ4IxoUdAQwjWOw3b94sV199tXFTxtILgzVjxipu5c477zTnmQCx6aIsuzNgHMHyjuWWz4xr2oU4XximseASM4wwBgA417Ms0FzTWpj5rFks0zX9h+ma8oBd63ZtGtsD/0XXL5dZY3cYS1Df7nukTQFvQ1eRbA4Vy85wYT/wS3mS0uzV/Pe+VfN0r6BC7/mP8CBNSizik62ra+TNZ6bKy4/Mlhce2FdefWymvP3iZAW/MKQn0ZtX3Z4nnv1xW81dqwbqZ5X0I28CwCXBcHKdDn5J7VM3u8TVn0MDRQd+RIFv8oXYcXjATQwhJXNnD1gmX09e9aPzJFA48EQV9+jEKWPkgCNn5KuaBhz38f/xCSUXyj3UI1AclLkXHDNgm/l2ctQxR2lcau9kux1/SAFvy+YSs6SDX8pQp+bYo2xxd92jAVidtz068ER+XNnfm1/TgJzoBFdvw1ADeQGAcS+dMWOGiQPN9TOA+AnBQvlexWWBPtYAfOJwcUWG6ApXX5ibyY0MeCTeGXDPxIBNcQThlVOIzUZs/CwxtVhjyd1sBeCMS7cFo1iamXiA7Mq6JlOW65NW6YADDjBVSdmEa3e9WpMR6wpNCiUE13GXBdqoQsKLn5F509YJltyhit8bk49M2irBNf/KOBkz1PZGQvnp/3GZxD0aQJSj6NdFwy0LZPwZp+RYIz+Kje9+TXNR4yrptPkONHZ1hdb7cXzXawMVyr9zzf8rxdM1D2iupGzqSu6viqrXR9/Qk/xTXOYRf2TeLNm/Jhk+k6kEL2GF6v78pa9/PBUyk6lcPh/b79SDpe7Q6eIL5PY7edp1n5GKsW5Yg/OeIVxm/CfPEG9RXwuvs0y/bZ2ZGf/pszT/fDI9Zb/zeXygbdkK2fJgk6y7LZlWKpGWEiMK8/vihKz5tWi5R/JYU8N36P2ng4ZvX3epZ4ArYjTJn5tJDj/8cHn55ZdToMqWsTGepMJ5r+KyQPdqEIspkxGkdCIOF6BJvDCCazICOD3xxBONCzr5dwG8jz76qCxfvtxYqLHKWrEWc9rDVZk1aYvmz59vitg4X0DyXXfdJf/4xz/McdInHXnkkSmgfNhhhwnH7r33XuMODdM0TM2AYsjHANRcH6HN3/72tyYl1fvBAk2fLbgGwA93ia5ZIlXBNjlx1ip5/K1pPfl/B+81ll/yBZ8wS9k54yWSaG4QT9WYwSuO8BKFSgq3qbtCJgV2DjpSwC9WzC3lM1zyK4e2Yi2N4ou0yaF775D/XcFvPSBYFZVVYC5PyLyZq8TTUSjxjlbxlvTGqGetlgcnElsWSOk+IYk2F0r3poEtl+KLi0/fp6uOVvb6nW+aSa1sXlV5oLqMQ9y46F0ZV6ipDDXy5V1VU5e+L9s7k7t0nGLjvYsS8sqdz8hHzvloxjby/SD31KX3flV+NPer0rRe089o3H4mKaoolqMuP1mO+NzxmU7n/bEp/3Gl7FzwjLSv2Ty4LvT3sWSv8bL3NVcMXjYPS+z810KJd4Vkxz8S0rlKQ+Tme6RkKhZzkdDGhOxYoNldjP0sJNufelomXXhuHmppeA95xANgfjix3uFWm0lwmSXmN10skRJgKhexwMVlge5lpM6mN8imrrvuuhQLNG7XuEqTEsnKV77yFRMrS0w1Fl0EQEpdawG2ZVl/+9vfNmRfbON6bON1bdojACs5fnHNBrRChAbgvvXWW41bMm7OCKRWAFLEglL6h3xQLNCwbTtZoDONz3RgmPyLN20xPTmkbot0R/3y5DL91R9EsPxWFnXJvx39qlqO9fP0aw7h5m3idQGwdDa2SovGZHWFKqS+TIGYvnjgEpkusBvDpr26tUL8iYb003m9H2vaoRZLv5QWtsn8fVbIwnfrJRzxZ5yc4V4MFkTl4CnrjReDx69x6lrfBcDJWygRSt5blUd2SfuyuHS+HZAEP8FKbJcSCMTUGBcYF5OKg7vMpIzACxBXoj9fdmtnqn4ebbRs3qlM2nGp0ccIS7fqEhCsRl9Nh5R0zUcdLZsa80grQx+qz++Tb7x6szx24wPy2A1/EY/D4sakYFQTgV9261Uy+8SPDL3xPKnR+fjvZa/KJfJ2YZl0dQYNVwe6cwqTrDxnCovDslfRy9L55B1SfNJFziLutmqga/MWdW1OGiw614isv5XpLJb+Et7hfrf7a2X3H8kLF+jZs2cbayPAJ1chPhWxrtCD1SPGFrEM0KxdFuj+LNDoCEDrZEgmjhlLq1MAu8QmW/BrzxEnvXXrVrtrrLGAZdzcEWJsSdsE4EUssF2rMbz333+/nHTSSSYemhhfrLxYlhE+Z+K+LVOzOdjzDyIsSLqsCzWTHdZD4P1ggSYdFIzbLEzIDHfxltWkuvjRqRvkvEOWKLgNSaESYKU/AAzY8Edl/4nb5IqjFyXBL7Vj6mJZWp1qJ583Nt7wfcNg3B4NyPLmKmlQ1mwIxEz6KAW9MX2mdiiY29xZIiuaq/UF2i+e9hbZ/tjj+ay2PmP3VVTrywj3n06UFcTkqBmrZPbELVJT2q73XMTEBgf0PhxV1ib76HHOlwSTvzmJaFi8Wt8V/fbqb3N087aUKkpnh2XUqe1SfmCXBCdFpKA2KsGJESndr1uqj++QysMU/PZMo8c1tCXekyou1YC7IUWVJcrw3PuqFdTNCtVZqb4yOCe6ipXEzZWBNYBB49irPiaLtNirnWFZEorI4lBYFnaEZfO4Ghf8DqC+0MJHpf2P39MZmJDUjG6Vp7eOkdVtSjoa9epEoU6u6sI2x/6xZYyMGtNqJrba775BQi+6z5p01RbU6DMjffYgvVDPvr+8LMsZ9/Du1EDPo2t3duGDv/ZnP/tZ49aKxe/JJ58c9ILEowJgAWGQHQ1FXBbogVmgie+FsRnLLAzJsDW/8sorxiKM6zPEVwhgFdbkiy++2KREWrlypXz/+9837tILFiyQz3zmM6acsy3coS3b8gMPPGBy+FpLMGReEF1dd911hmCLSRHuBdyacXHG6grgxluAa1ohvy/XI50T/UPebxZoG4dM24xxuItn1ER1dez97Z81bofMUFKsVQ3VsnxrjTR2FEtYUyKVaeqUqbVNMn1Mo1QWd/cZViLc5Vp/VSM7n35aupYqAZuvPPkiorlUt4RKzcJkAul6Yjob3+s0iRoTUhUIyTr9HlUceogEHJ4TnM1H8VXVag7aXrdIn1ooJ1a3mGUwfRC75StzY9zQU2jhE9K9uUt8U/SO68FsXjXoFtVHzTKQLmNt+gJ990+l6orh/xs20Dje73N7HTxN5/swoQ8g+iK9z2mHDlDAPWU1UFgUkANP2Vueu3eptGtYDQIO+eR/zLNF3HWaBhJdndJ227dSR6uKu5RHIiHPbuOdxqN/SctlMjt6QkqDmuZMyyCJrg5p17qFc44RT+HApE+mQp78qzz4I7L+tjvNZP5AQ+ZdqeKQgwYq4p7bTRronZbcTR34MC5LSqPp06fLU089ZVLctLS0ZL0s7MGwDePues0116RYmbNWSDthGaBZuyzQ/VmgsaxDLIXVEzCMezGsyIBLmyYIq+ojjzxijl1yySXmM5g7d65JTYW6SXWE/PnPf+7TlmVbJs0Qll9SJlm3aizEX/rSl0zKJepizcUKbVMQYQ0GIANGLQM0a8uOzWSIywKN5jRVQgtgtq/fFJaMaWN2yukHvCOfO2KxXD7vNTnv0KVyaP3mfuCXNqJhfeT2uLazn48CP8GGX98iMf2tmVDSpmA3/SUZ6y8/0em6Tsj4En310/j5zX/8Yz6qrt+Yozt3aL5VfVlLvsf1O5/tAOWj3QmJtiR5CLKVy4fjfB9b7rhRutfG1Zo+tBHj+dy1xiudzz4kscZeC/LQWhmZpRs2N8s7W5NgItMIzT2r/xYv257ptHssTQPzDj9THnryr9IQXC0dvp3SrsvW4Dty7TXflv/61R1ppd1dNNDx5O2SUMuvFRwSvnAYXnMe9ULQ77uBwJ6eZ5BH/nP+y328E+IKgjv/7j5rrP5Yb1+8yuT4HuyZw+TMtsVrXdJPp/KGyTZvVyNeYB/GolheXm5yxQKGsSDeeOONJpUOaXKuvfZaQ4qEpRDwC1vxD37wg/dFNy4LdC8LNLG3pANKj7smNhdrK4I7NPG6kGTZOFyOv/3226xSrM+QViEW5LKNi5R1R8eqC7AGaANwAcC4RyO0D9C2TNO4q0OiZUGuZYGGNAsCrLq6ulRfuIZTbPy3PWbP2zXHATt2cR63dfakdeeSNzWmbdd7TDxhJFoi3cuT6ax2vaU9u2b3xo0S0ZRgyAR11S1WV129U8x+tn8QiU0qbZNijWFN6Pek6R9PZyuaV8fbXlig91Rwl8YciQWl/cVnd6nuSKoU3bxG4m1NSn7lk0ijTxI5fsd5AUzohFbXeg1w1R+GrsXPjSS1vOex/O5r98gODWHY1JkMayC0wQqGYWKC32j2yz/vfVnWLd9kT7nrDBr4wfd+LitXrNZncVii3m5pDTRImy7xnrRdN3z//8m//velDDXz+1D3C4/ojdbZRwkVRWH5/sf+JYfXbZYxZR1mOUy3v3PSC1KkYSR9ROt2Pf/XPofyeScejsg7v/+LtHUVGu+DgUBwi5ZpWrZaWpavymeVDcux54ULNJrHtRYLH7lzn1a3Q9h+WdIFcEJe2ZtuuslYJ9PP78q+ywLdq7WN+tKPq7IFqfYMrspMPAB4ydVLSiTYub/whS8Yyy9WY4jJsNqee+65phqWXuTmm2+WSy+91JBm4fpsLfzED/N5YkXG9RkQbFMs4Q3ANkzQCNb6xx9/3CxcC4CM1Zf+4DoPaF+rccTI+80CbRrdQ/6ZGMFtGzU2OyBFpd0pN8lcu8+DIqbxrdGOiITfXSZFBxyWa9URV65Nf4+YFEGwoB84eps8t3kisyUKc/vPTfp1pn5McYdMqej1YImFQhLevkPdoEeNOP0MZUDtCxeoRTws3UquFlT3vbQ5qoxNofquUEDv5W5pX/i/UnnSJzKWy5eD4XcW672naEyl9aWg1J7Rya04oC7N7atldj5TpFZjvYmjndK95CUpOe6T+aK2AccZ1nvyjWeWqVoTsrXbJy0aY1ml4KLIp7+Dqrc23d+JN4xa4Cj7/IOvyuRZEwZsM19P8lt55+1/EThDsklbW4f8/nf3yEfnzc1WJO+Om2f2lszgC6B75v6Zz6UrKrp5lfHa8mioWL7LzjdXmOe00lVKU2exlAS6xe/rmTTQ7zVOW3ENXeoIByWmmS/ioS7Z+s8XpXL21HxX3bAaf94AYLSOJRaL3uLFiw34ZQ0gww0WtmfcbM8///wUSNqVT+qhhx5KVQvpyylACgCMJfKUU05JnePH/Pe//31q326QJgimYwAisciQLx199NHyq1/9ysSqWpdd3LR/9rOfyUUXXWSqcl2s1lYAfl//+tfN+RtuuMEQLP3nf/6nsXynE0vRl69+9asppmxcjLHIfve73zUAFIZmJg52tS+2T6wBuZmInrDOI1h9sdxifadPfEbE+SJY8n/5y18al2n26fekSZNMnS9+8YscMkD1mGOOMXG7ti1SIlH2F7/4hTz88MOmHED6pz/9aYrMypJwYRW2aZnsg9bu03fkrLPOMh4FeA7gdn3UUUcZ6/WOHmse5bjXsBzj6o0Qc0ze4gcffDA1RnNC/6Hfl15KzlrTBqmfhqsYtt0CZc1t90s4FJdAcW5gg/HoR2BepkOtmjdFIV5EgXQ+S0SZIXFjtgIIPmr8RtnSUSKb2sskFPPr41U9B/RpWlYQlsnlrVJd2FueeryMRHY25j0Ajm5PMhfHlJVcPZolWIg1PTN44z5EuruViVxfTpDojl5iPXMgD//Fm3ek3CQBsw33F0vl0V3iL9fpmAzEzrg9Y/ltfr5Q6+nN2yOxHa4V0+pi+8ad4vP3AoaQMmnzvc4kke6orHlzQ6ZT7jHVwM7GZgmF+nJJZFLMW4ATV1IaiLc2KmN7gT5Hel2gUyeHsOHx+SXR3iSe8l4SzCFUH1FFQ1u2K+F98hnD87k9rJZg1bDPq0YXXRO6FDfhS8lhw0/Rvtb9XRxuN0HmX+Lh1sv3uT/EeTqJh96P5uvr6w2wwRppBRCLe+7JJ59sgCQADgFsA7yJTbYCKCVmFYAO2CU1zrHHHivEJAOaAGjkxl2xYoUhceIcYJDzLFbq6upMO7Q3btw4ufLKK42FFBdf3L4BtrghW6EvtPnOO+8IBF6WfOrggw8WFo4BpomFPe2003LuC6Bx586+eU0Bq8TeWmBJH7785S8bgilIphDrTgyLMwRVTEjwWaEv+gYop0/olbawAlMWqyyWXyYdsNQuUOIq2xbt4to+Z84cM6nAxMHnPve5PkATFmjco5kEQR+MmfFTjljj8847L8UCTe5oADcu2+Q0xnr9iU98InU9O0Y+x82bN5vPg/Yt47SzX/Rt6tSpqbqUH87i0ZjtRE+u4nCXpkfRWc6gpkvQX/2BLUVqWIory2Rna5EOL/my7NHPL5/FG9Tx633mFEDwhFKNCdYFV8lo3Ct+fahyPJMwscNnku/i1EFMAUYo5JWAspL7SLmVJjG1uoXDeh87XlA8OqmT72J0YJivenSm3+3mBYUSGKsWyylRKahRCwfzBXo62uqV7vU+Ca3Te8+ZHgklurpM3UoFQe6z1O6gG4Ul7n2YTUl+TYMUUyAxmEBm6UqvBjzqFfOeYpZsU3HVPW25It4ChU5pz26AcLRnQjWTirwBV3eZ9LI7j+UlAP4gFI6lN10AOqTsIQcxANPGlwLmsDT+4Q9/6FMFiyFgD1dcwGq9gmrkscceM2vcgdMFKzFAE7dhBPBHHlsIpWCwBpgBBmFbxkJMKiibGojy9AWwBygE5GK5xFX8hz/8obGEL1u2zFhisWAiufaFMThzIlP3rrvuMmDxrbfeYteAPkAnwB/CKQRAC4iFaZm0RJdffrk5jjWYviLkzGVsAE/aov9s//jHP+7XFuX5HLAoM7lghQmBefPmGZdqjmF1Jgb4wgsvNBMZHIOcC53hOu1kgaYdrNLbtm2jmAHKWMetSzZ9wXJNeiYmG7g+LtTWQm/Lmcr6D3Bthc9qOEt6rtSIWtFwaQYE+3w9L84K1ng28OJnX/7CoQKJdPEA6EFy+pLsHzVuOA/1A+9bgX5nfQG/xEI9rlNpVwT0BqxO086ldjWdVEFV8rufOpaHG7Hyvi7gCZ046O4OqplXX0uUSdujrNAJnYBhwiZ1D/boiXs0Xta3fh6qULyVo8QTLJZEZ6tj+Oqau1W9PXTJVfyjJ+ZadMSXq51YrdkFen7zBhltsCQoM+dOHaRU/p5ufWeLBLpjkvTDyqwHoG/Rqv/P3pnAx1lV/f9kJpnsa5vuS9KNUpZW2lJ2KvsmioCILC8CoqiAgH98XxaBV+T1BUVeUURRKIjKoiyyCMguaxHaUrZSuu9t2jR7Zv+f753cyZPJTDKTVppknpPPk2e79z73nnlmnud3zzm/s10a12+VslGupRIt5RRpCp4EsJZce70c1cmxnIJY2FkvJQf96dKJ44z3VboD9RbmS8Uek9Mt7pb7jDTgTpXtZEUD+uyCyy1ux1hDr7nmGlm9enX8asxS2nJYNHGDxfoIcELsGsAK+dNJJ51krJiAMgAW1kmsu1hTLSiNN64bv/3tb2XNmjUGHOIKjTUaUIvFKFHoi2U7xqX3iiuukKamJgPcsURj+UUy6QuWZsbkXOg3C4AdwHjjjTeaOF/aJi0S12Z8WGDJx0sbCHG9liDLgkuOp9MW5Z5++ml54YUXjEv47373Ow4Z13QnyZlNs1Srkw5MTBAfjo4t0ZaTBZr4YQA4a3L3ApD5HLBwI/QLizTLvcrSy2QAAJ/Yc7wAGONAlRy9j/InxSZD7BgimvKoTS27LQ2FyrPhU9foPE01qK6out3eVCAt9UUKfrFsdL4I5vjypXDv7I7TKp01U11Iu7o0W52mu85Tg3pe5cC9n9IdZ2/lFq3Kl3a9D7sLoFfd0cJK6mQsvp33oC3brucWrcEtP7slf8/9dthSxMt2wT5zs1uRjtEzOXvEWQdJXkHv1h/uzAO+NNNR2920Gti+apPce9xVsl9utT2UdM0U7Kz8ofLA1/4nLWtx0kYG2cEcj1fyJu+zw6PKmzwzI9C3wxfsxw2UThovvpL0vTXUNiwjD9PfV1f6lQZcALyTPw7ItuyC2ytxn1deeaXAKvzqq6/Gr8aD0ZYjZRIuzddff72JG6UQQAwB3FIWV2AsyVgtAVOk9WFNbDHWUmdcL+UBtcT+cpzcthA5Yc21VkvTeJJ/9BNLLW0gWGetZNIXCKYYk3MB/GEFxcILkRUpkQCNgHIs2RBfcd3Ro2MkIADhVatWye233y577LGH6QYxsrh2I+m0BTC955574nrDyo4Qaw3ZlWWZZhKCmGFyAuOSjEUYndE3UjXV1NQYUizGYPNDY9WlHfqN2H4D5NH7YYcdZqzo7H/3u981nwnbVrem0gD8V/z5EyWnMKZHZ/exumERDiixUKA131h8wwaUxO4lZ1lPcank1UxxHsq6bU/jCikoDem4u09KpaOMHHWNLq/YopbN5BbkdNoYDGXCobD844Wt6n7W/T5LZ3xBvW+feWZTl9/QdOoNtjJetQDnjpmwY8NSEq38vfbfsTYGWe2zrj1JKoeViceb+v7MzfPKFfd8U8uVD7LR75zhvPZ/j6jbqVf2yKuUA/LIXdtV0GyeTrCeXTBRKsUnjWu3yNJnY+kSu5bMzr2CQ5WUzhcLweuTBtQzpHBuZ8hen9oYRJV4h5u6z1p9p+vweutlbGNq6qSguG/P+V6adk/vgAZcALwDyku3arI0SAAky0TN+g9/+INcddVVhiQKQijAMVZQrIwAaay5uOjCWgypE8DtmWeeMcRauFlbCyl9wtr7rW99y4Dkhx56yHQTV2gsmonxp84xAH5xNcatGMvtxIkTjQXz3Xff7XNfnO2zDWCEBIv+Y10GWGIhBxQDOBFAPfHOkEyRughLMCAeAXRCQoWk0xakYbSDFdwpAGmArCUVg/mbbeJ9cVvmPFZ0BMs818Vqjfs4eiLumDLoHXZqxE4WcJwfSCYM0CWTDrhnMylBGwNdSo84SaL53QFwuuNSp12pOu+KrJ9NDn3wvAwbt1VfjNN7iCbql/jWyjEhCa98N/FUVu0vfXOpRJTk5fnVo9QKnPkj7blVoySa45Vlby/LKr0lG2zh5w5OdjjNYzpZOKZGPEkmx9JsYFAWK6kolruX3GymucIEUDskovv8nXb1iXLwybGJVMdpd7NDA588NV9Jh2ITfQf4hsmpBTUy2VsmFTk+qdRl79xKOatwkgzzwjGh7O4NLfLhI6911HZXBfudoKmi8nVCP3NdUCeSUyD5c47LvPIgrRHZ8olUV62Vafus7HGEXmWGHjK8QcHyegl//Pcey7onP3sNpB/Y89n3bdBcMVkaJCyLkCRZAWBBngRIghjKyQJ99NFHGwIlXGexNgLUIF/C8ksuYyyciSzQuEy//PLLBsRB2oRFNhULNH2w4HfhwoXGcgzoBLgTdwwYtizQfemLHaNdM25iaK+++mpzCAsr13Dm8yV+lnhmXJNxHbZCPayzVtJpC72yAP4t2RZt40KOnhH0iUs2zNCXXnqpOcZngc75PBALcGHzhpDLyQLNRIUl/SJWGRAPm7OTBZqJCyYtAP+Abyu0RYw4gut5f2aBpo+4LwcOOVdyHrlJ8jIEb2F9/9seKpfJhx5PU1kt0W1rpLC4TYaO2S5bVld2uOimr5Jx0zbqJIKmn2nekn6lQViycUujhANhWdw0RIYoS/ZBYzanNcqgkje9uWGYfLi1SorKw9JU15RWvcFcyKPPoVi8NKNMbbHsroPYm7WnoPN3rXuZ7D3iK/DJpopmCW0NiDqJ6xRgrsLeqGjSKAmpZahobN8nFAe7VtsbW9SbSIkWHTLeWyIsPcmWj1f3dDqrzkGE1VDwOSlvfVEZoTNDwTqXr3X3kZEuAVb8nols+lgdtyIyZsJWyS8MyUcLxknAryzZRrV4YMYmumqnbpQJuogSRofXvSu5s/8j3oa7ses14ALgnfQZ4LKMdc9J/IT7LbGtsDQ70yBZ5mXnpbGAspDWCNdZBPdh6gKKAH6450K2BZCzaZCwLiZjgQZgJ6ZBwooKsE1kgcaCnCwNEkAzkQU6k744x+fcxuprGZIB8IzBxvvacsQ9E1uLGzeAccGCBSaFkSXjsuVsW+ieGGeAZGJbtiwu11jZESzsJ554oj1l1kwUYDG/4IILDAM1Vnos0MT0IjYNEp8PkwlOFmjSGGH5teUgJbNjBDhjxcciDAC26ZlMYf3H2KwVmYmIgSDBivHy/voJMmfsp2l3F8DRGvDJwsY9ZHbatQZvwWig1QxuyKhGQ9BUt65C171ZMDXVglp+x+6+UR+8+lSNauzqDsYRD3QN+1sDJlSBcby0drS0Kwv0QaM3SZ4nrGkpuo+OSRjcnl9aM1L+tSnmTsnvJe1ku0QDbeb+CmsapFhK4HRAsL71aTHDuB0cGL9fu+Jz9vuDCnjbpUkXp+SHfdKkIM+V5BoItQfVSybJFzl58fjRsOrblU4NBEP5snbNEBmn7rgANYBtT0IZMjes0zqFe6Uf79pTm4PmXDD27GY81SMbZOiIxdKs6R1blAsFnRUoKWhFVYvet6pEKwH3O25V0V/WLgDeSZ8EwBSX5T/96U/dWoT1FzZjmwbJshknFsQaiTUX0Ix7sBVYh0mDhAvzueeeaw5jqcQSjOWXGGIrWDadLNCJaZCSsUDDSJ2YBgn3YtIvYREGHFvgmW5fUqVBwtoLORQuwZYhGXdhSKos8Kc/EFQRi0v8NC+nVnAFZ7IhUYjtBTRz3URhwiCRBZrPC/ZlXJmtezUAFysw6ZeswFJ9xhlnGJdyrMgIsdyJLNCAZeteTjkmMxJZoLEAI7ac2dF/TjIucj4PBCkaWi5bWsvk1U9rZOb4tSZVTyprMB8f6Xw2NJTJgjWjZOg0ZaV0RTxDx8e1MHRMg8YIBWTTyiESCkLaxAuf4w0lR1MhKZNxUWm7DK/dJr4CYodV1PU3p3x4bDtL/1eOqFA27U4CrDc3DJePt1XIgaM2yeTKBuOlENaXEq/qL6B6XVpfLq+vHy7bYYnuEK+mWKnQdrJdPJU6IZCXr9mO/KIE4x0gGK047sW4kmK/y1iMedHjhdpbNSJ+1t3oqoG99pkkb72yuOtB3fOrdXPWAXt0O+4eiGmgqKpU78OuruPp6KZkRFU6xbKmTP6I4dK0IE9Wrxwqw0ZsV26TsHmmJAJhC3yDQa9s3lCp77W5UjHc/V47b5Sc8jHm2WuPocPS8naz2GNd1p5cyRkyscshd2fXayCrATCWRwAPQAwAiHXwkksuMa6xAFDL6pvJx4TF1AqgiLhdYklvvvlmmTp1qnFF5jzutTAIIwBngNtLL71kgGCqNEiwM1vBMokkpkGy57kmFs3ENEj2vHMNAHWmQYKBGkIu+o9OcHu2ghUT6a0vqdIg4YoNeATkw5BMeqa3335brrvuOmMlxzKL5RR98PkAGhkH7RGLDKgEMFrWavoCcGVCAMuqdWnmuBUnCzQkYlhccbmmHm7ip512milKjmHIxegfgB9rLUzeuCjjCm4twbieQ2oFOzb3DSCZ/tr0RpSDsAz9McbKykq56667TGoqLmTL2f4NxHXVxFES1he3umiJPP/RZKkZuk3GVW6XQl/QzC6Tbob0M7w4b2nSyQm1tDW2x5h2i0o7gcdAHPvO6rO3ZpZIQZnGH8Tiwksq26S4Yq20NeVLkzJnw6Yd1peQ3PyQFJb4paSiTdNNJVg1Qn7xjt5zZ3VpQLZTu0+thPwdEwIdIwDcPrlinNK9q4q9ISnKC0mLvsj51TqcTEKBkIyfruWzXHy7z9G80horGNT7KldTR+nbcEQ9N8wcZAzvxjXkBL7mYH6h5M88In7e3eiqgdmz9k4KgDW6UqbsUdu1sLsX14BHJ6eGj/HJmo+7Ws7jBZJs5Grs5eTZ2T0xmKiWqs8fKvWvvCpBfe9Zt3qoFBX7pbikXQqL9LveYakM63e9TQksW5oLpLWF57ROHJYUS+XcQxKby+p9z1h9dmci+SXi3e2oTGq4ZT8DDWTuV/IZdOqzuASW08mTJxsXX9xPAWRY5lgAZbAOYx3MVLDG2mXmzJkZs0ADlpDENEi4P3OOGGAspVh/zzvvvKRpkAC0AGzch60rNMAwnTRIWGghqUIPgNUjjuh8obFpkNLpC27IgEznAjDEDRn2ZWJjaZ/rEWeLizi5ixGss7g9I8T/AngBvwjW49///vdmm39MXpx11lmmXWthj5/UDYDpHXfcIbNnzzas2bAwA4KPOeYYU8zmJAbs4saMazOkV5Q75ZRTTGwwbsm4Mds8zoyDNEiWBRoLMXq2DNOUZ8GVmjHS1sUXX2zK87kN5DRIVref/OlptfryRhw1yd8/3VwtLyyZLP/4cIr889MJ8uaK8fLikkny1PtT5e1V4+Lgl/JNHy+TYHOnC5FtM9vWuVMO7jZkZpKLyvwyfHy9jJ26WWr22iBjpmwR3KS7gV+t7R29h+Z5zG7m2OLKYhm319huurQHcIneppMvqcAv5SbMrFW97wBLqr3YAF/njd9dPOWxkA+Gwv0IEM7Ni0iur+vCcc5bIc4wf9aRdtddOzTw7EOvywO/fEbKpczE/+r0oHj0r1DjgYtyCuW0z10urc1tjhruptVApGW7zKheJL7crpNc9nyyda7GYNa2P53sVNYeqzxgf53cshOAGo7UUiBbNlXI6hXDZcWnI8zCNsc4Z70++F5XHugyuztvnJy8Ask94NvqLRMjXXOe67bN+/io6eIZvU+3U+6BXauBrATA5OM955xzDGDBAuxMTwRQJO0OAkjDIrijkowFOlWb1j22tjY2I2xTD82YMcO4Np988smm78QXA/gAjolpkBiDkwU63TRIgDbcnUk9dPDBB5s1OXGtZNIXgJ8zBRLb9BfACnAEkDoFwGyZrLHm2vJYX5944gkDcm15S0rFPnl6IbCin7BnJwpWWECwk3SKMhb4Wj3D/ozYfbOj/6gPSCce2oJyJhicYj8ze8yet2uOA5Dt4jxu6wy09aqnX5eCnKC+xnWVgIKNJgUb9a1F0hbEZbxrCZ+68npzPbLxrfe7VszCvZzCMvEpmVg4p/ccocnUE80rlvwvXZ/sVNYdO+1HX5X83HCfxk29r1z/lT7VHYyVSs+6Ulm1Y94a6Y4voh4fhYedLt6K6nSrZE259ja//PjC3wpeBoDeEv2rUCgMGC7QP6zrW9bXy8O/ey5rdJLJQIMf/FNGVzfJpJFbNIwhve/4Uft8JLl1H0ukMcbLkcn1BmtZr4aJjb3wmxLJjYVypTvOcd/+pnj13c2VrhrwHaaZLMrSCOfSL7jvrAd0srDru1DX1ty9XaEBOx20K669y66JZRfSJJh7sajiauuUb3zjG8Y6DAgjJheX2B2RZCzQAKt58+aZZgFG9AcLJGvSDyWmQQKkJ7JAP/744yYNEn0EPJIX10oiC7R1hQaAJvsiWvBrWaCxgqIHXHghcNprr73iKZky7YvtE2vin7FkJ7oqY1XFJRmQiSUVt2RcldE9uZRxKUYYl80DzP73vvc9Y2FlO5lARkVKJT6DP//5z8at/bHHHjMEWwBym1KJPqEXrM1cn37yGeFWjR4AwVjAEazQ3ENMRnCMFFZY3K2gS8rfeuutBpTDbo0LNECZhTE6ATn5ji2DtAXZtq3+uCYeq3XTNvHo73mxzso3h9IBcGpFUpfoQgUbWH8bVqyX1Da7/jjqf0+f8o/6ntQ9dY+UeOr1HsrsGvXlc6R8wr6ZVRqkpSfvOVT2GtEoC9eXm3jzTIb5uVHbZcIewzKpMqjLFsw+Wja1jJJhvhUmbrq3wRJf7dfJruFf+8/eimbl+cVvLe113K3N7fLsg2/Imd/7Qq9ls61AaNX7Em1rkoP3bBImWj5ZN0zX3X8sc3RyFR6KQ/b8VEZWNSnbcYWE1n4svmkHZZvKUo535FdOkVd/er+MltUpy9gTEZ2YWVUwRQ449WR7yF0naMCzZ7GmN9L3nzoNTSJMHac4K6CrQq949hypgf7rNA/zZHvGXfcTDWQlAAbkYTUE/KYS3GQBLhb0pCpnj1srYDos0AArQK/Tlde2A/nUT37yEwMQie8FEEOMRFxsMhZoWKMRmwaJflhLYyILtE2DZPtKPduXZCzQxEFjSSbGFRKvTPtC+4kCyMXFGoFRmgWx1lvLkIwLOteEJRqXaSsccwruxQjtEEudTIgbLigoMAzd6AZhn/he645Mfl/SDxHTa8vYtizgpu8IoNmmQSKel3uFCQgnCzRWf4C0TYNEzDJAHmIxO0bbPkD5xRdfNLuAcKzn/Vna65vEm6/xqf6AeBXUluQGpDVEWg87w2nXjCKmbyy/gF9zRGl4AdCuqH28oFQeWniYnDfrrwI7cTpkpwHNdbt5e7GsGvZFqXGVaDQQ3r5NjpmwWuqaamVlA78JznswuZJ8ak2qqWiUoyask3DDNvGWZrcrudVSqK1dFi8qkppRw2TiqM06MdPV1dmWYx3Ue7FeY/wXfTJOfK8skDGHdfXscZbN1u16TdMVCvVuudy2pSFbVdTjuCPb1sfPH7rXMpk4sk7e+XSsbG0s1klYjVHX7zrf9rHV9TJz4hqpLO1wJVca86hrAY7rzm4s3DZUNvhbZO9KnXTV57I6ZHWRkD6HmGB4b1uVbC5xfxO7KCdxJ9wq3glFEh2l99o2zUbQrMrjHbNAfT0qFRiXetXtXAkaA1u1pguAE9W3q/ezEgADlJwWuGQfAhY94jVx101HrEUzHRZop7XQ2TZtvPfeewbsAj6xQJPiCNCUigWa8840SLQBwCWeljjVRBZo3KHXr+98oNAXQHAqFmjaAnRC+MSSbl9wO7ZWTTtGSMVSMSRb5mULzrH8YuG2gkWVz4y459tuuy2eM7g3Rmnq8xkC8L/+9a8LLNkQlRFnzISAZYEGAJMOCiAO0EfQJXWZfCAW2rJA00eAN0zPMFbTB0Cz7bsdIyDb6pfrYtlHbDmzo/8gHLOkYsQz93cpHKJWNn1JtuLVt48StQSHdHae9DLM0gN78fjJY1Ze47GwFjulqNpl3EUfbUs+kOCadXLdJ4fJOUe8KyMqmqTAl/xlWb+mEgh55Z1lo+SZf02RuWsfksj3TxJPkZtD9J1H35FKpS0+d/rH8o8VY2T++mHGEhxOYinympe+iOw/eqMcVrNeAko0tvDxhbLvhbXOWzQrt0NKbPfEFy8T/QrLqg3VUq8gY/KYTVJW0qbPCYjtYuEcrNsDebJsrcYMbi8z3/eXv/dTOfHxW6V0vFo8XIlroGJIqbSrhbc3yY/HZ/ZWMrvOe4aM6TLgMUMbhAXPg1Z/nvFSgHyRe7KLeBR8lLku+U6dNHywRKS+Tj4OVMmKpjKZWNoo40qa1VAZe+a0hb2yurlEljWWiT+SK1Xtm6Tu1fky9KDUxiJn+9m0HY0q4ac+S7jtchTw5oxKHjYSjajngiKthHmGbFJVvx1rVgLg6dOnG/dUQJeTTdj5KcEODIMzrsiZiMsCvU1wO07FAp0OQzLAEvALML/00ksNYdX8+fMNEIWMCmsp4B4BKPfEKO387Lg2VnXALwJAtyzQ9JnjWKeJCx89erRx/wbwI4Bc6iOZsEDjHo17dU8s0FierTDp0t8lrODXEw0bjx/b1xjYjZrcq/ZYqjXs0KEtm1OdzqrjG2+9QcaVNcmaphL5zdP7ymS1bhyw+2qpGV6vkyo5BnR4dQIhqC8mn6wbKi8uniB1CkqwXg7zbJatD/9Jqs/8RlbpLHGw7Y2t8tLt/5ATRucI2ZCOrF0rczQFEiD4w7oqafD7zAQME/PlBX7ZQxnLZ4/aIqX60oz4dVLh+Z8/ITPOPlZ8xclfYhKvOVj3lz7wjDSt2WSMGExiNbYUyTtLak3sZXGhhm74Qupp41Xg4dOJA7VwdAhlg00t8vaNd8lhv7nKHs76dVBz0d54+E90gkBnr3qRpqV18uYjb8t+J7lWdKeq8ibNlPYinWRpjU1M23OkNSstTJ27OxoKSO74zN7fbNuDcd2+uU7+df7lMqIgIo06eQUp4Ifbq8ySbLw6lS0j8ltl0RX/LQc+Ok8KhnWS4yUrn03HotGANGw6X3IrqzTWfJPkdHgWJtNBVI03LW2/lZLiGuU/GZGsiHtsF2kgKyclzjzzTGOZO/744w3hlXXDtZ8BpEunn366cZOFCTgTsQzQrF0W6O4s0MTHsvTEkEzsL+C3trbWuBsTqzt37lxTh8/CugunwyiNhZbyWL0h94LgDPdqBEvthx9+aLaxMCMwOjNBAtj9zne+E7f6Yg12WaCNimTd355VKyUvdLz29kH0YbHlmef6lNuxD1frt1Va318g/jWrZJLmquVlDsD7yfpqmff8TPnvPx8mtzx6oNz5zGy56eFD5McPfl4eem0vA34ZUIUCuYqcRtn2wDyJhpK7/vfbge/kji186BUJtQdlRX2lWn1jZqCy/KAcUbtOLp69WK456B35/n4L5GpdXzTrfWP1teA3qGk/lm3TeoGgLH7k9Z3cs4HVXETddBf94n4JtbR1+2aHI14DhrfUl8n25uKu4Fd/BvgliKoP//rXFkrjyg0Da+D/xt7+7ed/V/b2fBmq3M89CS9iRSGROy68W9rSsBb31NZgO5c37UB1x8qMuEmd9iVvyhzxFLsuvPZ+WDnvAQmrcWF8cYu6Pfc+IaN+WzJOy4Zb22TVvQ/aZty1aiDQ+qL+4EUkNGq00UdPb0KBiVP02d4ibY33urrrZxrISgAMwCFFEBY/QA5xt4h1rcXVF+svsbjECu+ouCzQn4+zOlsCLrtGtzZmmTXHYbhGnJZR9mFiRnAHR9JhlKYsMcykQiIl05FHHmnyClOfczU1NWzGCcRwQbcCUMddHgAOILYEVc6+UzbRpdmet2vKJI6RYwNV1v/9JSmMthrXn8zHEJWi3KBE9KHavGJ15tUHUY3mN19RPTSbHLUzhm3Rl5JO12disJraCmTj9lJjbUsc9sFj1plDUX0It32040z1ie0PpP33FbgGlWn3A801HVIX/GSSr+Q4yYRJh4+2VEtAQcfiR7MbAG/7aIWEdSIgJup9oBs9GDZMMc4b8NvxaxBRpuN1r7zT0UZ2r5jEffIXz+rLckCKJVdGdIBgp6cud6sGysh4/U8ka6AtIG/8ZX52Ky5h9Dm+Qmmcfoa0ah7vdIUgHN8X/1+6xbOi3IannpeI8nb49LfwwGrrgZUMukWV2yMiR4xYZ8pSh7qudGrA3/KU7vg1xitP2mfMNCeiaiSx2oyq+31Uvfn8U3aXaGGRnlewHHhfDTsxzpvOltytXamB5G8Lu7JHn9G1L7vsMpP+COZkC7iI0yR2lRQ9L730klxyySU7pTc9sUDPmzfPpPCBkOlrX/taWizQWEYB5pYFmthVm0LIdhi2ZNibf/vb3wppn2CBnjVrVjewZssD9oiThSAMoEhcKjGrsECTgxcgCGs2bTpZoNPpi70Ga65jGZJJRQQDN0RXgEgWQKYlnYIV++GHHzYuzrgq33TTTaYp3JWRdBilLQs0VmD0dOqpp8qSJRoHo8I59IRAMAahFWmVyJcMyRUxwwBX6iC4XyOWBRrSK/TeEwt0qjGahgbov+alK4xLaamPuDb7k5/OYJiXV+boPI2dUeDWtGR5OpUGbZnWDxYxe2LGt+/IzTKhHBKcnvUJacmJk5bJyJJWUy+i3yf/iqWDVkfpDKxuWczi2KZs5C8tr+kVtNk2AW+Pf7ybukDHXqy3LFlrT2XlevuSlRKJA2DuxE4QnAiEDfBV/XG3dpLf6e3c7pct73yUlfpLHPS6JRskqBMCVooUBAN0qxUIV+i2Ok8qKM6XkeJTTcdgcVtju/zriQW2irvu0MAbm6fKO2uq09IHXh2/nL+fLNuUHn9LWo0O8EL+um3GkmuHUZIXMgC3Or9dszMoT0dO2Cxsc+zQYRt1orpzQjakmRtowxX9vQs3KJDd3qkKb66077OvBGonSmjEKAkOHynB8bXSvsd0iZR1eiCQ+Tvkj3kcdlZ2t3alBtKfUtuVvdzJ18a6C+nRgQceKLjRArwAvwDhCRMmGIDW10vCxmyFWFaszABg8vUee+yx9pQBVn1hgbYxoscdd5z8/Oc/l0QWaHsBrI+pWKCZmXYKIC8ZC/S1114r559/vgHEkE8lskCn2xfntWBSTmRInjZtWheGZFiQAaMIEwOME7GMzdbi6mSUNgU6/iUySts0SLi6W31RlHZtm+jrd7/7nZx77rny9NNPm4UyWIzRAZKMBRoyLHIm98YCnThGa82mXbwQXnjhBTbNfZFo+TYn+tG/UEsMfBlW56hfGoO9x03yKg1jdFVBmyErYVbZvxlmxOyV0NYtXQZ/VO0aGVbcJv/aMFyJxNRDQd1OlV7DMHVCJFakEwefH7dORpW0dHnuU0IAAEAASURBVNYLBiS4eWPnfhZuYb21srG5VB7+cHc5fson6lZOWhQgWlfhBTmoun1xea00BfLjJ9saHHqNH82ejdbN9epK3jWmEhCseQViBC4OVcaAL7px2jNjumpe3/W+zh4Ndh3p9o0N3bSjtEyaBbjn166ta12g0VWTIhuXb5Z/vDZN6prz5eipa4xlMjfhu90e9Jh4/jtfnyaftlbL4RsdICWxwSzb92v8b04uz5NOAeDuX71FAmGPpjKM3ZMAY18S92jqAoDzh8aMD52tZN9WVMFvjnRae40GIJOtqDJLKo0Y0iwncE5V0D3+mWmg51/iz6wbn+2FADq4xZI/9+yzzzZsv5aht689wRJKepuf/exn8SaIGyWV0jHHHGNAFK60yJgxY4z10loWOQYoxcL43HPPpWSBBsiRMxYACEM0LtuJLNC49NIO7WFJTWSBPuigg4zrMNdE6AttJrJAYy1mgdQLcAhZmJMFure+9MQCzUQDzMrE5dJXrMrEXSOMDbbn7373u8YFnVRCXBuiK+oBjC2DNzG81noPuMWCzufgbIsY4JtvvtnE9hLzTcwvqa3ICexMN8QEyMUXX2ys3owTSzX9e+ihh8wxYoMtC/T+++9vygJ6scTjHo2F3QJz26+exmgG2/GPfMI2JRd1+rvkOIi6CvWB6fG0GSABaIvZeJ0vxbEjBd6Qkg4F4kydHn2g5pZmN3uxp7D7+GcMq1OSpq2yXkmx6tQFulWJhkqUrGmUMnUOK4pNHnS5P/TB6y0p63Io23Y8CXk8mvz58shHu8vUoVtkQlW9FKv+wuoaDSBuCfhkucb8frxFQxqUBMYpuZraK5vFV1IkHmUidlqBY/qIWYLT1Y2vLDZ5mW75wVquqKzQTGhmOr6i8th7Qqb1BnP5siGxe+rhRRPllU9Hyecn63vD2DopKwiYuP+tzYUyf3W1vKIM+a1K8FRSlSPFrh7jt0RuaYnG6HdadOMndAOX6Cpv14kv53m2o8oPkO3Pa6uTHA/fz+S6tGWSrnOUKdrUTXrWPbgLNND1DWAXdGBXXBImYASr3M4SyyzsbA9AhNvtBx98YACmJVHCdRdgdvfddzuLm7yxxCfDoPzVr37VgDkKPPnkk6actUQ6KzEW2IytWzBArr6+Pp4GCWCGO/edd95p4lwBf1i/rdAXAC7WUIAm1lniYHEDBiACGLEOW12l25eeWKAXLVok5OYlhhcdkWoIV3TEWn6ZNMASi0s25ZA5c+YYIiub+xfg2VtbAEost4zpvvvuM+3Yf+gOtmdALFZ69Mb4rUs2/cIjgNRWfB5cD2GSAvZp3LcRJgqwjtu+p9MvU7Hj32GHHRbfhS26v4uvStMgNTXHu5mvKRR8Ba3GYulXtmLABm6RxBHBVsx5SJ6c4tFJjvzqIc5DWbedN2yEtH/cPX43T3U1vrzJLL0phRRIuUNi92VvZQfr+aKqUvE3deT+7Bgkbs2LNo40C/GAhWo9b9PJhNh0THJNFA/J7omEwmGVkluYr9wINg44uZ56PKrPktLxLtMpOhq120gJp5H/16lPr+YMnbL/JOchd1s1sNu+E4UJhdbGNqlrKdTc6ZPMkko5IXU9nzB9XKrTWXe8YPhQQ1LX14FH1aBSkOXPa6u7HE9Vj88RWy5xjdXYkxsjzUo85+7vGg1kJQA+9NBDDYghLhTwsjPl17/+dbw5SJawIhKHe8011wguz9bqSH7YX/3qV6Ys8bVYTAGq1hUWYIc1E8BKzCqxqlg5AYO4HteopRerIazGgNLE/LFcE0sluXdxhcbSDajF3TlR6ItNBwW4JCctABXgfuWVV8ZdtzPpC5ZmG8trrwcwZCGGFnBJfLEzRRAWcuuSDPgFtNL3Qw45RGCGvuGGG0zu3UmTYi8I6bTFxASTDU5yK9sfXLyxpBPzzHmuD5BN7BflmVjAWo4QC42FGksxlvOrr77aWKLRNZJOv0zBAfpvxJEHy/K7HsRtIT4Cfe81QBewm45EgyEZMnt6OkUHbZmyzx8jLf96XYmwdsD1VieQimcfNGh1lM7Aph4zS9688+9Kytn9t436gN7WoK/HpnK8+ht43Oweywz2kyMPnCGRDgLCvo7Vp14d446Y09fqg6peYUmB7Lb/ZFn07GJ97qY3tILifE2DtHPfSdK7cv8uNfOYvSVXJwfSEZ5FU+dMkophnfGX6dQbzGU8Pp+U77W7bHuzbwR1FXtPE9pwRYM+crySlz9dgu1vqjo634F61U1OvqZBGt9rMbfAZ6eBrCTBwtUVayrgCrdZ3HwBkYDPxCWZZbenj4e8wXbBtfeEE04wIBKXWsijrGB1teVImQQQg3gKl18E8IvQL8qSuglAChjElXfx4sVmTWwxeWmdcb2UB9TCdM3xESNGmDhTrLnWamkaT/KPfv7xj3801+S0JX5iO5O+APQZk3MBYAKw6RNWT8qwD5ikv2zTdyYEcCUHQDNWjjPuyZMn0434pEU6bUGcRUwt8d64RrPYyQLIuCBDQ5hUwDIO8ZXtF/eJdbdGh2wzBtbkIQbEwyBu3ZexJiPp9MsUHKD/Rn/hKJ3JTO9lJPkQozL0wFniLew9djh5/cFxtGTOQRqXtWNzkIVKtJFbUTk4FNLHUez15QP1hWTH3Jepv+cX9+9jDwZHNV9pkYw8YIa+4fV9PHmaR3n47D363sAgq3nmjV+RdF2aczw5stdh06R2uvuSnHgbFJUWyrk3nZ54OOk+kw3f/uV/JD2XzQcnfefrklvSPeymN514tQ51XenUQEHJKbqTwfuL3pMFJV/R99sdeW/qvL67tXM0kJUAGMsfccAAMWJBYXsGqB5++OHdFqyBOyrJ0iBx7QceeCC+wCR81VVXGXddSJwAx07m5TVr1pjUTAA5+glb8jPPPGOss4ks0MQef+tb3zIgmRhWpDcWaMoAfnF3hrkYq2gqFuhM+kK7ToE5GZALmLYMyYBUADDWVwRgSv+xFD///PNCHSzCgH7Ap9VnOm2Vl5cbF2Ys8GyzWFDLGrCKYIlGHnzwQeP2zRi5R7DiA46ZmKB/gGT09Jvf/Mb0izjg119/3dS1kwXp9MtUGKD/yqZOlAJDsKm/6n0QXvRGH7FvH2oOrire4hKpOGyuxgX1UY9KAlN93kWDSyl9GM2YfSZJcSlkVn3TI/XKKgtl1PQJfbj64Koy8wrlxOjjW4FHSe72/vbJJo54cGml76OZuE+tfPeuC3ptIK8wT/MF++T7D17ca9lsLXD0uYfK+T9NDYI9euOWaqzwHR/8RMZOHZWtako57qpZ03XiWZ+7+v6ViVQfsp9Uztw7kyqDvqw3b6zkN2kIpb7H9yrET2/0SX7x4b0WdQt8thrYMfPDZ9vXnXY1Yklh+01HsM7uqCRLg4QrspPwCGsi5EkAwcsvv9wQcxGjCug6+uijjeUS92CsjQAy4lax/BKvCpkX7NM2jpb+4jJN6iDrCo1V8wc/+IGJZQV8J4oFv8TcYjnGhRmgmcgC3Ze+OK9FzDFWa6yyF154oTlFfDGgHkAPsAeYAthxH8c6T4wz8bXoDDZqAD6SblvolYV4Y1yW3377bVMfq7ptC/dyYrRxRf/mN79pzqNnLLy4o9MvC3Bh8yZNEjHFAGhYoHGLpp9Iuv0yhfUfrvBcAyEOub+zQNPPyrKg+BujEjaYI7MHakWZMk3m9/Etm4sPIqk4aJY0PfdXzUPLPZ2BHhVslI8OSMG4mNv9IFJJxkNhQm3s2BJp2KzpKTLRYfxKOTJufHbH/1pVVO5WI2OqW2X1RiVwykCX0GRV6fe65pA9bVPuukMDuDT/6MUr5aen/VK26T0KE7SViGo515crs46fIZfce6E+j9zfRaubZOuTLz9eY3vHy0/OvF22b2qIF8lRne52wCT5vk42jJrkxqDHFZOwsffNP5QNL7ymKQY6CSkTisR3w/qa6CkokBk/uy5+zN3o1EDuqm0SWfsvCR6mGIHvbcd7abwErgga6uVduk7ylqky94mfcTf6iQayEgBDaMSyM6W2ttaAVWcMMAzFsBfD1OxMg2SZl53XJ2USC2l6LCkS7sPUBZxBukSuWlyyAXI2DRIuzZSxUlMTY4EGLCamQcKNNxkLNBbkZGmQSNWTyAKdSV9sn5xrCKkg7EpkSMYiDACGSZvrAoK/973vyYwZM0z8NHG8WMVPPPHEeHPOtpgoALw2NDQYHTnbshVIeYWVHaEt9GmFtviMbN5kgDJx0cRcA07pF2UQPh8mE5ws0OQsxvKLOPvlZLpOHKMprP+YkEg2KWHP98e1x5sj1WVNsrGBOCtQcOdLXar+8pJcWugXNXRkPAudqs2Bftzj8crQSX7Z9EG+EuYoCI6moUdlMy6uapXiofrQzXA2f6DrK1X/fQW5MjJfJ6n8uCakdz9SDrgxSlNz5SkIcSWmgeqKgET87bKmviotlZCbuqygXWrHpBf/n1ajg6zQnnOnyX+/erV8ffoPxNMW1ERIMWZtv/4mzlHwe8VDlwyyEf/7hvO5I/aUA84+QP70syf04cl3PSpRNRyccuWJLvjtRe2wvEenzJS2hf+SwtygeWonPkLAbWi1PZwnhbu5qC2lSlVxuRu2ieex1yW0+ziJjB8uUSURNAH/avX1rt8quYtXiKe+SYlhdkvZjHti12nAfep36B4LH9ZX3G/7IgBTXJZhDE4UrJdYFYkfRbAQJhOskVhzAc3/9V//FS8C6zCuuBB2Wcv1LbfcYizBWH6dVuqVK7uyQCemQUrGAg24BPABdrFs/uUvfzGWz0ceecSwMDtZoNPtS09pkAD6iSzQWIARwL1dQ3rF9aygY2K2cT8GzNt0Q5zHRZkYaqy0p58ec5PKtC3uAQCyZQmnXRt3TFtcDyGWO5EFGgBrr0e5dMZoGtN/F13U6cYKQdtAkIjej/p1kREVDbJdWTn9hmGXnncHcDn6ggzQKNcctwWaNikS1Jc/zQPsimqrdKgSD0Vk+KSt0ri5WFrqi2KEOVE01lVyFPh61F26fESTFJb5ldWzSHIKXcslWiqsKpNc1c1oBbObNQ1SyLzEdb8XrUZhhs7T+7I6P6Bs5Vo/yxmgrV5YewqLpLpks+RrntA19RUS0jyhYXM/dtUnwBe35xFljTKstEUibb6sZyR36jFxe9zkUTLloCny9kvvS0gtQ1Yu/tnZdtNdp6mBMy49Xv5w8980+0Ce/ibmSIHGnh9wjMavu9KrBoZOnyyL578vzUoMCAgmRaFXv8dIWCdg2zU9XFsoT39DPTJmr0m9tpetBaIV6pWY6xFPc5v43l4iwqLCHHaHOuOqiZS5UCuujH60kdWfyieffGIYfLHyrVq1yrjY4gZ7xBFHGCCElTBTAURaARRt2LDBWBXJRTt16tS0WKBTpUGCndmKJW9KTINkz1vX58Q0SPa8c40LoTMNEhZPwCT9BxDi9mwFKybSW196SoMEmRdtJ2NbtqmEiPmFkMzJAk1cMtZVXLtPO+00w7ZMHDHHmBCYP3++SWtkY4n70haTE4Bb3LKxItvPk7asuzSu504WaHJKA/jt9WCBTmeMVqcDbd269FMJ+8PmR17fPaSqpE2CYb+0af7Fdl0i+gSI6uJRwJanOQYLNQ9rgaahic806xTztldfl6EndlrgB5oOdlZ//QU6cxzwKzukThAMb5GSIa3S1ligi1qEg15lNlY95qoe80MG9BaU+uN6bGwslnLNLeiKMpB7Qjr1ou6kej+OzG+XtohXmjQVkl9TciFAt9grHmXDUqrgrlDX3JPUA+y5EtNAsE3BmSqrrMAv00ZsEvIqb28tNGmkgprmjHzK+bkhqdS81Fh+bYqzcDC8Q6lWBrv+l/9rmfg+WSdFetOpTUjy9MdzorI+P3r1n+Xb912i9yJ3qSvpaGDoyEp5peleeez3L0ih6vALX/98OtXcMqqBNcvVdVe/32Gdlm4O5pslqWK0zOZNnSkzk5bJ0oNh/yppL3xO0zx2/84mgt+ofq9D430SaHhafOXHZKnG+uews/btifjNvfbaSyCJwrJYXV1tiJBwY73//vsFS9y7776b8aeGNdYuM2fOzJgF2pIx2ZhQm3oId13OEQOMCy7xqcTD4mZtQantLA9SrNnE8lpXaMBbOmmQsIBDFIU1k7hhJgOsZNIXXK0BrM4FYEisMcsFF1zQhW2ZGGjGRJwzYPKOO+6Q2bNnd2GBJjcwAuhFaI/Pi1he3JttGiIs6MTmZtIW+sICjGWaGHHqn3LKKaaPtl82jzPXdbJAn3HGGUbPlsE7nTGaAQzQf3V//7taMLrGkQN0y9S9eVh5s1qFm2RkZaMM1+2qklYDgO27He5VEQV1DW+8KRG9D7JdVt7/pGxcX6Xft9iD1JsblZKqNqmu2S4jJm+VkbvVyfCJmud7TKMBwFaPoZBH1qyqlq1vLcx2FZrxtyx+L64HdFSk6biGq0v0WLUIj1JAPEy3WbM/XK2+nLe6BO01vbsgXj+bNxreeUeC/k4NoCOA8Liq7bLb8C2y56iNsvuIzTJh6DYDgC345XsdVJfJTQ8/2lnZ3YprYMOS9XLD3B9KU12TTNJ0Pp9T0LZnoVrgFIm889jb8qf/d2+8rLuRngaKNM3U6ZccJ186/3A3djo9lUnd0vWy5OWPpEUtvIDgVML3uUnLLH5qgWxdtiFVsaw97t92n6hLoIT2HqcW3+4g2CoGFYcnVEt0qE/8W+fpe6I70Wp10x/WWQmAcROGgAmLHTG7WBCJgyXOkxhSUvAALAF/gKkdFctaTDxob2LdaGtra01RwC2AllhYCJJOPvlkOeecc0z/cKlOlgYJsOtkgU43DZIlwsIaDrET67vvvjve5Uz6Anh2pkBim/7aWW67pnFAul04jnUYEGzZmm0HLPC1uoH0C9CKDugnFnbawSrOREEmbdEOYlmh2aYfuIczGUJbtoyz75SznxnbiD1v1xyz42PtPM65gSb1L75gYq8AsjwoM5WwWuVydKKlUV+2s102PPuKrF4xVIIBjc3KQJf124pl6wavbITQJMulftFHxopblKvELnE7b0wpvJvgGp2vVkvW7HeVqBSrG6AGYEvDh0u7nsrCvfpXXpVQi3p0aDx6uvcj5fD4CAcisvW557NQa70P+Z6Lf99joTfuf02wELuSmQY+eW+ltDS1ZVYpi0t/8Lc3JNjml3q1/Laoh0zsu9upEPYhtgT8UibUFpD3H3ujs4C7pTqLSKgVIlXlbqkdJqF9JxitRB3WYB7lUZ3oiowfKmFlgkei0XYN/Vputt1//UMDWekCfeeddxpgAzuz08LJR0IKItyLJ0yYYJiUccPF3XZHJBkLNOBq3rx5pllAEW67ECmxJv1QYhok4k4TWaAff/xxkwaJuGEImXpigbau0Lh7JwNgFvxaFmisoN/4xjeMmzL5jLGWw3TMdqZ9ceqO62BlvvXWW42usbzfddddBkQCJAGZkGQdddRRgt5IU4Vl97HHHpNly5YZEP3lL3/ZNAlDM2zYTAwweUHbAGfkO9/5jlmn2xafARZcWLWZsMAVmphuJkdsW5YFmvRMxCEzGYFVnBRWWJCtpDNGJ7h/6aWX4nHhpFnq7xKsj+kYIOtRN1Iemt2BRfdRUC4E0RMwRT/noH5m2S7+um2G/GrBOxNk/4OW9KrLYNCj1nevfLh4vKouKs3L12S7CqV9c51ElXSkWN3sg+r6HNCFe6x3iZoYuCKtFw3naztbpXza5N6rDeISbStWMvMnoYjSNKkvX656dvT03daies/q5KCGPqDzgP42utJVA6FASFYtXNH1YMJeu8YRfqixwRNmTUw44+6m0sDzj7wll335JnP66VV3yMhx1amKusc7NLDm7U+UgyNmhdwWLFCgq7+bGs5AWAji199OgHEwym+ogmGNVaeOK50aiIYgO9WXmQ6JjBsq/iGl4l2tKT7rmiVHfxQj5UUG/EYri20xrRJW3a8Rb0F2P2M6FbLrt7ISAONeDMBNBL/OjwOXY8DZiy++mBYAtlbAdFigAb8Art//vvusMKDuJz/5iYk3tWmQsEDCmpyMBRrWaMSmQaIftM2SyAJt0yDZvlLP9iUZCzREXFiSiXGFxAtwnklfaD9RAJQATMCmTYNUU1NjXMUh3bIs0LgiFygFP/pkLAhu0v/3f/9nJgJsu+jquuuui7eFu/KPfvQj89nZMsnawhrtbAtQi4szINeyOdMWngDcL9wr9B0BgNs0SHgRHHLIIWYCwtZLNkYYpck17Ryj7R8u99xnSLLJCVuuv6yjOjEQkxxjKfLqS7JHHwipXpQ7Pj4l01GrUgcwiep9Gmpq7i9D2iX9iARUjx3P0YA/T157ZapM23ONlJS26fcfwqvObuEijcV988YK+XTpyPiJYJbrEEUEG5v1NyLmkl+hbs6NAXU3U3dce6/FlRXfiJ0p8AalNM+SsUXF1aW+8DYTnRqToBLbRSIhnbBUd/GOY/Y7br/TYSXICuoLs51wcMMaOhTlWPlblGCzl/RGEOG11seeL46q7mYKDQT0t/N/vntn/OxDv31WLr7hjPi+u5FcA23buz5zAbrbdUK1J2lPqNNT2Ww4F42gwwSdaUhDePfRPQ9fAXA03FX/PVdwz/67NeB4xfp3X6r/tI+VzcZr9tQryqTrAm0JkmCBtsuDDz5ogA1AzskC7bQWOq9PG++99148P7BNg0QZJws0llmsogA0UiCx3HRTbCaUNgC4MDmPHDlSYIHGdZi+4ArN4gRZ9AUQbFmgAXrUBZCRn5a26PsXvvCFeLqldPqCJXb58uVdFiyqMCRjAQbQQyaFazfHsXojFpwDdgHlDz/8cNwCi3syoB53dSvE49q2iJEGLBN/7BTaqq2tNQDXHqccEwK2Le6J1atXd3G75vOnzdtuu820a1mg6aN1lcZFGvBMe7bvthyfu/2scbtnnIgtZ/tCvPOSJUvMMhByAHtVL52iro8KbAG3MWsQFqGuS1iBW8yl0r5G6+uy3gc+nWDKZvH48sSbH2MWRw+hYK68t6BGFi+sMfG927aWSGNDodRtKZXlS0fIO29Nlk8/GaXK7dRj4fDs1iF6KxhRLTnh2HeL/TJfQMp9mtqogxgLt2idPtCFtbI/q7WjQs+Xarm4hALi6lLD2jScxClhZYRtb9cJBbXw8h0Oaux5QF+YA3qvtunxoLpKWvBLvdyyUmd1d1s1UKxWoIC/k/U5mVKIxyypdhndk+km8dhj970gh447R7a3d0zW6PzLvF8+Kl+aebGs+rT3MLPE9rJpv2xkVcbDLVPCMVc6NZDjRR8xi3nn0TS2PD7Jyc1c/2m07Bbpoway0gJMrCgsybivTpo0KanqAKKAFmthTVooyUHLGswpgKXLAt2ppD/+8Y9dmJsBrrhcjx492liXKWmZlG0tCMpwMUaOPfZYkybKskDbMlhcaQtXaDsRYc+xhpEZEIsrM0AesI91e9u2bXFGaay/XIu0R+QfxkKMq/zzzz9vwC0gF7CNZMICTd8B0bh5M7GAJI7RHBxA/wrVeyLRfRk3yJC+LFuTJhAtZtzsBGtdhqgouXBSbNKjy/Es2ynbfbLUveGMhVaG2MYis/SmCo+C5yH7fa63YoP+fMnwEnUtA8x2zuf61ILu87abyRjSeRhWcnXp9WrqHmvFdCoGJu7iES54K5s9S+pf+WcCQV3M+yDSwajt1FvidslemWdOSGxjsO3f9JVfSKsya/v0pzDZvcd4CR+895pHZPpxM2X0lE4Pj8Gmix0dz90/f1RuvOzOrs0wt9Cgz/l3l8kRk78hj/zrVtlzputm2lVJsb0R4ZWyVCcACRVJR5gsHF3R/8Oy0hnLzirjya1Uj44qfb/P0GNDvZS8hXvtrG647ewEDXS+MeyExgZKE2effbYBpyeddJIsWNCd/ZN4WhiAsbBSJhOxDNCsXRbo7izQ6TA3Y1Ul/veaa64x8dgAZGTMmDHGgvzhhx/GPxJims866yzDAg1oTRTagokaC7CTuRnXbuTttyEz0NiXDhIswC+TIqNGjYpbnjkPsHZZoNGEAvgeX3IBvDFbm9MyZCo6/6kVvbCmxnkkK7dHH3eoPkxj7ruZKiBH3VOHHzon02qDr/wnL0iBpolKJgCOPCXA8inrc66uUwGQokKNA/74+WRNZNWxSiUWzNHnXl/Eqx46Qx0p8/rSxmCr8+GrS+TtJxZIcxthSTHPmGRjbFAep3Z1lb79m3clO+0eUw28+o8F3cFvEs2cd9y1UrepPsmZ7D4U3LhOhm2Zb34H09UEv5lVHz0hwU2uZd2ps7wKxQU5hc5DvWx7JLdouj7ry3sp557+LDWQlQB4v/32M1Y+XIMBqcTdQhKFdXCPPfaQ/fff31gJAWDTp0/f4c/DZYH+vGGEBqCmw9yMizWg9fXXX5fLL7/cxCDzIWC55VyNAzj9XVPyQIQFCzQEZolCTHEy9m0nCRV1LNEZLOBWqIdrM+7OAHcLkp0u5JRNdGm25+2aMrRjF+dxzg008eb37QW5c5zqiqoPVldEhk2pUqKhmK08E33g0ltRpimmhrkuVYE3HpaacZt0kqoPbmmqdOqN1/r+Nx/J5CMYlGXzR46QIUccri93md6TUfENqZSqQw8ZlHrp66Aeu+XvEmjDPT9H6gG5uom7s12Ug0gAv6EORv3lC1bJ+qWdIT59ve5grPeL6/6Y1rBalVDsod8/m1bZbCrU/PqL4o34Zf+xa/RuTO/5+/naFeKNBoS6rnRqwFd2jHjyRug7HRP+6UhECobGjC7plHbLfDYayEoAjGpvueUWQ2QEOF28eLFxa33iiScE6yIWQEilIH/aGdITCzRM0IA3CJm+9rWvpcUCXavWTECfZYEmjhWrtVOwXMPYjKs3sa0A/FmzZnUDa7aOkwUa8Hn99debuNx7773X5EMmfjUZC3Q6fbHXYJ2KubmtrS1ucYW5mVhkrgnotHHY6BErrNMqj8WWfMfEOycTy6oMg/QDDzxgXNL5nC0BGVZlBCIr4pAZ93PPPWfKXnnlleYcnwuSyAIN6RV674kFmkmWTZs2GXZpgDKLBdKmUf2HezUpp1gg3erv0rZ8mVrSeIBm+pLMyGJ1vJoiILgVNsXsltCaT2TCWGWPNPpMVxc6maLuvuNq2iW0fkW6lQZtuci29Rpm0CJlSh6WmR5jKqkob5GK8laJ1K0dtDrKZGBjzz5ZfPmZfr9zZMLXj+2z9TiT/g2ksliAOyVHWoMKhFtzZLt6ldbr0tCuoSMKfq1ENWzqg39+bHfddYcG/P6gLHkvvd+69taAPP+3t1zdJWigddF8iWqox4iSFjly4nJz1pPkucMxnzckhyn4rdAc4FF/u7QuinnKJTSZtbs5Gs9bPOYWfd5ElbsjtRrw+vC3eaV4/F3i8cU8GVOXds981hrYUVPOZ93fnXo92HxZsPp9/HHsoUMMKAAsU7FWQJcFupOROpUO02FuvuKKK0wcLkRcWE4RLLE33nhjFxZojgEasdZakinndS1zM2mdaItYYYS0ToBjXKQRrLJMQvz4xz82INgc1H9HHnmknHnmmWbXtrWzWaCZaHGyQFuXb9uH/rYOKmD3qtVSsXwGM6CMgs8xx1jcPCSRV+u8r3pYfxveZ9qfcN16KVJCpt1qNsjHK3hAxnSUqhMAPJ+y8k6tWS/e3AoJb6+TvPG7pSqeFcejATWhqUzdbZ28/c5E/T0AUHSCCnMyyT9SeMFwvNuUmHtftK2TATlJ8aw55Am3SM3n2uSTN0rMhEJUY6hTi8ZV50Zk7G7bFDT3zQKfuu2BfwYG6GQS4yHvfsav4K1JU6m40lUD27Y0SJ4v/ddV1wW6q/7YC9V1pigbWtQmX9ztY/mobqisbSwTv2FyF8nXlEhjyhpl96F1UuzrRHahuk3dG8zyIzneUnnoli/L+ClPyZ6HKtO75pkv6OAHhZIi0JYjG5fnypO/GSdXPz8uy7XVP4ef/i9K/+x/xr0CqGJRhA3ZSnl5uQCQiAf95JNPBBfpTMWSL8EAnSiQHmXKAg1Jk2WBJnWOk3mZ9rFgO1mg7TWdLNDEvMIC/bOf/cywQH/1q18143a6BCeyQJPeB7ImLLVcF31ZFmhcxNPtCyzQkEw5BeZqGKAtczOkVZCMkV4pkbkZJmUAqgW/tAObcyJLMjmJ0YW1ElMOkHzppZea8paRedq0aYYMywLeNWvWmLHZiQvq4QkAMZpTrNUZAi3bFnUA3litaS8ZCzTnLAs0ZGgQqiUyXdvrXHzxxXLGGWeYXZiv+7vkqRUeiYFgUnpZsGHXyUYQm8TA3ZQ4zKhOWuRWVCQrmFXHvFU6AeDxSlFBUKZNWCtrNg1RwhyfchQQR90JPJiVZ7a5Uq2Vo6she1MrsPpResoqs0pfyQabk5evlo028wKy76xPZcnSUbK9vljIU50cCCto07jrqspmmTRxg7kfaTcnvzuHQLLrDfZjOSVVqsuITN57jdRtKJcGZSNHnCRYTMTwPS5Sq/uw0dslr7RQqOdKVw34CpVBW0FtupJXkCclVU6W/XRrDu5yFVUlEtR8yulKeaVLaJeoK29l7LltjwNwZ43aYJYOG0P8t9CWsevcyhgBqN131zENVIwaLvdeXSXl1UHZ/QC/DK/R9xqfyLYNXlnylk/WfpwnE2YOd9XVTzWQVQD4vvvuM8RKuNfC8Jsov/nNb4xrLKl5KEs8cKbiskBvMxb0p556yuTwdeoPFuhx42IzYb0xNz/99NPywgsvmLRHuKSff/75hqX5hhtuEBYEUHnttdfKnnvuaT5XgDs5e4kLBmgDJi1zM27mxxxzjFxwwQUmzy/WYMradFiJbdFPwC/lmBRgEsC2tbNZoAHnVhJjk+3x/rQuqKkF/RL8HANi+vQkH2jMepm8p4A3Zkh5aUbI25rXwaodO5Kd/3PHTlYujWKJtjQaIqfJGovapjmBG5sLzZr0Uvl5ISku9Etpcbvk5TqsbEqClTuyJjsV5xh1Tlm16m+7OcL9NVUtus0t+bJ23RD17lHdqjdvjt57UZ1UyNHbFJfnMaO36ne/q3XOU+G+qKBEzzD9fqsFne8r4LZqeKO0NRdIe2uepvPJldy8iOTrhE2xgt+8uNU3R3InzjSfgfuvUwMTZ9bKwmcXdx7oZcub65Gp+7sMxolqKiwqkHETR8rHi1Yknuq2j6X44KP36XY82w8UTpsuLW/9k3x73VRhn8vdTnBA36UKtK4r3TUw8wsz5ZlfPSP1GyPy+sPdJ1AJ9dr/tP27V3SP9AsNdJoY+kV3/n2dIE8ubMEAHax/yQTQQ4wpqXkOPPBAwbqYqbgs0E8alWHRJZbYuVgA2RtzMy7N99xzj4nLPf744+MgFav0G2+8EY+hJVYXy/hFF10k6B3wiLUdaz7gFeuxZW5mDSCu1tyzu+++uwHUdBQrOnL//fd3aYvyWMyJEca1eu3atfG2GIeTURrrLdeyYJp4ahbANvcU1mKsvFiwuV7FALd8lh90iDhzAfPwzFU3SKxqvDTHgG5szbHY0gl+0XfJ3jM0XjCPzayW/N1nK9bomBXo0ERhflCGD2mUmlFbZdLYzTJ2hE4qKWjrAn61rG/S3uIp6P7QzTaF5u97oqK2rnO5JQpuAcL7zloqM/dZLnvtsdqs2cflORH8ijdPfHO+mG2qSzpewkF8B54W1ynf7VJNhVI9qkFG126V4WPqpWJoswP86nzYiAniHT4haXvZfPCEi46SorL02WKH11bLuD3GZLPKUo79gh+covdh79ZxLMWnXXBMynay9UTJ/nP1eZH+vWj15MkvlJL9DrW77tqhgZoZNXLAaQfob2H3dxmPTmblF+fLUd8+ylHD3exPGsgKAExO3//8z/80AATLLtbJZEIsJu7B3/jGN0xc8HnnnbdTSIlcFuhOFmj03htzMyCRz4nPzCmQTgFyLWg9+OCDDVFZYi5n8u4ConFVtqRSWIStCzNtQjiF+zfkYIiN77Wu7Oag/rOs0LheW/KqRBZnpxs19ex5u+YYANkuzuOcG2hSstfekpckdhccZwEwLroxINwN34mnuFiGn3H2QBv2v6W/nqISKT5gX0UQGTav5UtPOTfDSoOzeP6Bp2jwWvIXO+5J4nyLigJmnTDXEFdIjq9Q8vf7Unw/2zfyDztPIiE1nacpeQe592IyVc06foZMmTNRPQ+6TnIlK8uxC+9w9ZhKN184fa4ce8pBqU6b41h/7372RzK2tjPErccKWXQyv3aKFO6tXhqZTDyrp1fRjH0lv9b1Skh1q5zzf+fI8ZcdL74in7o/5yongnptKfD93HGfk1+u/KXZT1XXPb5rNZAVABiWYMDHrbfeamItAT6pBJAEczLxrsQD//nPf05VNO3jWCORWmVvtkJc6Lx588ySTSzQjL835mbK8DlgUQVcLl++nEPGeo8V34JUmLAhqXIKeoVQCrdogDLxuQhM2Vhhzz33XEN8hqv6l770pfhncs4555hr3nzzzSY3NCmXIMRqamoyDNNYb/9dLNDO/g+U7bGXft8A2Yz7qwikeNqeUjpLLZ+uGA0UTYtIbpm6Nqebekat7EVTFdiVdabsymZVeofXim+G/g4kWIHT1onW8806TrzV49OuMtgLBlYslfa2GEGdPjqTCsdxL2+pL5L2FSuTlsn2g0x2Xv3E96V8WJnkpkgfx6RMcUWR/M+r18hucyZlu8p6HP+P77xYLrzqNMHfwzlnyH55WZH86uGr5KAjXffnVEoc+Z//o9wblYZqMVUZe9x87fX9a+SVP7GH3HUKDZxw+QkS0Fj/kHofhDW3WaAtIMdedKz4CjQg2JV+q4HUSLDfdjnzjkFuBegF8KQrV111lSlK3UyE9El2ATxDyATAHTZsmBx77LHxpgDkpOJhueuuuwzxFK7ZuPnedtttBuThgg1B1KGHHiqvvPKKHHXUUXHr53HHHWcAYU1NjWmTazqFBy8WVMAiEwAAwx/84Adx8OgsS19wD/7ggw/M4XfeecfE0BJfi+UUN2ZicvvaF+e12MYlOF2hT/azwB35xBPV3TGFMA6ItQDNl1xyiSllybGou2DBApNLmDK4TrNvLcQQVMFOTd5gC5RJ+8R9AxBGrJXYskCTjgnd4O6NWzMWalsOqz+f+YUXXiinnHKK+axOOOEEc55rOIV808Sls9g2nOf743bZ7DlSdewJaT1Ibf95YdaZKJn001vjVnJ7LqvXbeukcq6m8DFvdSnQRoeCcvKiUjghKCVTW0XatmS12pyDL/mPmyTqK+X2ykhMeY2lLj77fzKqN9gLhzavl3BruzRvLpJQu9cAXX2EdFmHgx5prS+UiN8joTXLBrtK+jw+rJJ3rb1NvvrDk8RbkCshzcFqF6UQlGmHTJUbXrxKph24W5+vkU0Vz7/4i3Koenntru68E3z5MlmXvQuL5OhRI+Tzx6s3jSspNeAtLZfaPzytv5OaCbiH30q+66GoTyb97U3xKEeFKz1rYP4j8yW/KD9eCILK5377XHzf3eifGuCVa1ALoAh315kzZxpAku5gSZMDiExkBU5VH+suwAbGZSuAT2JOIV+CxKmoKBavBxgjpvTUU0+1RQ1AxcJIXCupepKxQAOASdcEwIMhGqD8z3/+0yy2IQAx7QB4yY2byAINWCMG1wp9oU0snlhFLQs0OYNZOIYeiJt1skD31peeWKDttbGu0l9r0bXHnWv097//+78GfBOnS7ztHXfc0YXFm/LE3NIebsoASsu4bPMAw9b8zDPPmM8IIAv4vf3224WcwHZiZPbs2SYW+KOPPjLeAuiFtkiNhVgX6v3339+AZHIAY4UmJhhXausKTTmANZ8jLvV8HuRk5lqILWd29B+THliYEYi8BooM+cJJsv7+hyRP43wRvU1SCkCDxbcbcasFKctl44lo3VJDzjT0hBZpXZonrZ/kqbIAG6pQVKvTlOjWUxCVkj39kj86bHQZ2fi+eKdlo8a6jzmnsESis84Xz6s3G5AG2VVvwkteREnGvMdcKbhAu9KpgfD2bYapnRuzvUF1o94JXl/YhDVwXwJ+o4ZlO1YnuGVjZ2V3q5sGvF6PnHrlF2V1yzaZd9MjkhOK6lc7Kn4Jy5/+dqmUlrkgo5vSUhx46Z5XxKvu+aPzulrXGjZulw9e/kj2OHT3FDXdw2iAOODNEfWaad0oZUXt+mzpioQBx42tBRIsGS0AZld614CJAXa8/3j0+w72cKV/a2DQA2DAG660md6MNrWNBSa9fYwrVqzoVgSgg+stVkwApiVkAvwCyLAMO4V+Tp8+3cQoQ8AEqEaefPJJswZEJwqWYdIN2dzFK1euNFZQgBRETQCzl156ybBeH3DAAQbQW1BIW/QFHf385z83IBfLJTHTWD0hcPrwww+NddgyFafbl55YoNEtpGQAcXTE9WF8Puyww+LDI40QeXnt9UgpRB+WLVsmL7/8spx2mpK0dAhxut///vdlyZIlQvyv0y3aEm9hXWUCgjKkSLrsssuMNR2wawEw/WLc7777rmkZIOsE57YtJil++tOfyqZNm0w5JgqwjkPAhVAOhmn6ZMfIfYT+EVvO7Og/50QIIH2gyKY7f2VSowT0gZkLAZbDHgxgc/7+h5QlOgygW/6JtK1YJoW1EwfKMP+t/QwtuE+iTY16UyhJmMYJFu8WlKLJQQluU301eSQSUDZeBb55lcq4XeogEtMZ5tCih8U74zLxVE34t/ZxoDTuq91NNj4yRkqKNomvMGRe7LgPE8VOxgRafdLiHy4jxk1KLJLV+4E1y2XrPb/qGiqo3/GwMkA7OMjjOkKf/pXLpenlZ6T00KPjx92N7hr47jVnyJ0/e1iiGsYQDIbkt49d54Lf7mpKeeR3F90jL857RfI0s5SGXIoNreYe3NoUkR/O/bH81+OXyawTXDfoVEoMNjZJsL5BApECaW4r0Ge3Plv0fkR4RpN5AMlpq5O2NeukcOxos+/+S66BqM6kzsh7UYo9jTqh5ZMC5ZzguXPm/u9L1N+i6fXcya3kmtv1Rwc9AEbFEyZMMMAH4GetsL2p3gIRctdmIr/+9a/jxXGzxQJITDGWRNydAZUIrrWk2EFgGcZiClAFCCIAKAAwFmhAG+62WDgBVMS21tTUyL777musmIBEYmOdwjWxVNJ/XKHPPvtsA+6STQTQFyy8CO7JV1xxhQFvAPcrr7wy7rqdSV+wNGOBdooFkIBHXJPvvfdeA8xxM7/uuuuMhZ7YXSYH0BdjBaxiQZ8/f76Qpoq+Asqt4DKMuzMTDYwtccICCzLC5IO9DqAbcAuotoRalMFdnVzAZWVlpj+vv/56l37ZCQxco7/73e8aizgTG1dffbWx+Np7hXECpukLYwSUWzd37r+BzgKNrlo+WCzNC9/RzyNiQHAwxE9JB/GVrplVjrlZaT5bfXnWx2nsfMgv6277uUy65Ze6n90SDYck9OINmsPWLzlFavXtCGrDeukbquZJlp6kuV1Cr9wkvi/d0VOprDlXPHN/CfrD0tRaqsQjYSkoUWt5ccDco3pH6p9a3XQipl2Br785X2O1vBLRlD7FM/fLGh2lM9Atv1ErukZZRqOxF7l06oTbQ7Ll9hul5IDPS06CZS6d+tlSJl9jAl9cepc899ibMnJstcw9dna2DH2Hx3n/D/8if/9ljE+lXadb9ckj/GwCfhv9ORIM84wR+empt8k1T1/hWoKTaDysHnJvn3yWePS7rf4cWgLAq275SWa2yD2/6MJLZfZf/yDe/E733iTNZvWh5huPlMj6JfKjk/yycNUwCegEwp5jtojnvag0XvSwlP7sY/GUds3BnNUK60eD5xsw6AULG+ARi2I6ghstOWsRpzUxnboAOLvg9krcJyASF11nWiWsnrYcKXxwab7++uvjqXQAvwjglrJnnnmmsSQfcsghQmoggBpr4kzJS4vLsxXKAxRt7O+IESPkO9/5jgGO1mppyyau6Sdjpw3EEj+xnUlfAPqMybkA/l577TWTyojYWMoQYwwgZBzW/ReLNuCX48TbUm/u3LnGgso4rVWWvsLYjRWbYxaA0lcrtIsuH3zwQWP1HT06NpuJxR6gjas7AnEWsc9YZ8kRjTs0scDOfsFATV9YO9MgMRGB2LaZRKCfWLQZI/sAZj4Ttq1uTaUB+m/Lg3+WiLqS+3T2uFM0rkjdIsMRr3moso5GYw9ZyngVLPNQbVm0QIIJcdCdbWTPVmTdvxT8tqj/vr7BBWMs4emMnomeaIPqPRyU8MdPCEDaFZ2UCeTK4i1V5kUYcNuyvUi2rdPY/DVVui6PrddXSKseN+BX1f7OxippCzrpdLJbk6G6TdK+6G38742OABc9CefDodh3PNreJi1vvtRT8aw/9+Cdz8gJe1wkv7r2fvn+6T+TvfK+LC3NbVmvl94U0LClUZ667dkuxVoCHtnS4pG6Vo+Cjtj7CgWC7UG563v3dSnr7sQ0sPmpf0hI33t4vWMKISbOLznbOpGt8Jgyga3bZPPTbixrh6K6rdoeuUEi6z7SgOlYTvkZ4zfLvhM2qHeCPpN1koEQktbbz9KfU+d7Urdm3AO7SANZAYBh/sUKBxAFCAKcUgkWW0AmsbXEfjqJq1LV6e14sjRIAKQHHnggvvzhD38wZE/Ep2KBBBxjGcbaCJCGIGvDhg0mP/Hhhx9uYl2xUtM/rJ/Eo1oBDH7rW98yIPmhhx4yh3GFxlU3Mf7U1mGNXiDDev/99w25E3G0WDBxCe5rX5ztsw2wxTIKwHQKFmM7Bpt6CKs4OmLcxM/ymSDWyguLNhZgLK5McnAcUGv1yngAm7g4Q3xFrO03v/lN0wZ6BZyS7xmxEx777befuQ5tAJpJf8R1aQs3Z7wI2MYazbXpM5ZixE4WcJzrMmGALpl0wOUbAEwbg0Ea34yNWYekLj8WgDkfpM5R6gNVgW++ugZZaXzzNbuZtevI8hfVdzR2P0Q2BXnv0AmDVDqMqcmcDygA3t6hS2UwjqxTwOKKfPDiB/LyxvHSEsozViGnSpzxqhwPqatffXu+vLZlvHyo9VyJaaD1ndf1Nozdg0xehTomBxJvS/YN+FWLekQnupBIc6M0v/Z8rCH3fzcN/PPpd+XaC26X1qY2adjWLH5liuU58fXDr+nxudytoSw8sODvi3SixQK23hWwecVm2byqrveCWVZi01PPSrhZJ11VeHZ7cxTomu8733n8ZDTWX5/VnEMou0lBsyvdNRANaajSP/9gJqK7n+04ohOJkQ1LJbLWfcak1NEuPJEVLtCAGIAcDMIQHxFDiksxMaWQGEFehbsvy+OPP24AC4D5kUceMaBlRz+fZGmQeJGFJMkK4A23XEDS5ZdfbiyaxPcCmI4++mgDALGIYm3EbZd+Y/mFIOqee+4xzNM2xpQ2GR/jtK7QgD1YoIktdlqL7fUt+F24cKGxHANIAe7EHcN0fN555/W5L/YarLHWoltrxbXnALGAf6zvuCYjuGXjJm71xJgBw1jzEZipEdyN+Xyt2PJY72kLKzyWW9oiBRPC8VtuuSXOSG1TLZGjOJkAxgHRCJMOkIWRqxiLLvmIAdRYrhHGiOWY+w5LN8K9xsQFkxaMESuyFci9SLmF8HlTrz9LRF37I2rtsUK+3wINygqoG7SGpnaTPLUS5+V2vrxEdBIhqC7w2S7R+pX6ztGhF8WzkVXqrjtGwZu+fOR4Oy0a6MkAX0BHi1LnbLETDnpCrcDS4r7ooaONH61UcOGX2xfuKSdPXibjy5okX2PTsWRYAbT5FbQtbyiTxz6t1cOtsnHJal3PsUWyeg2ZVbQ19oKMIgDBAXUv9ep3OEe/51aXEZ1AwJ085uHRqbLghjWdO+5WFw08Mq/75ADf61VL18vGtVtl9PhhXcq7O50aWP7uSmnXkI90hYmFtR+uk2Hjh6ZbJSvKta7it65T+D57DQlWkgd3R7G2lV3rdNbO7q3I1lX8QPaqhGgoIOHVi8U7bu9ey7oFPlsNZAUARqWAIIDof/zHf8TBbipV47oKiLJxsanKOY/jZgt4ccYAwwRMrCsWRACRtSZb5mVnfUifWOgf10dwH6YugI+4WSyZuO5ixSUNEsRVWBetZZQ6NTUxFmgerMT+0h6u0IA/XKGTsUBjQU5Mg0S5UaNGdWOBzqQv9CdRALnl5Z3MgoB9wKclEWMywroNUxaQD7AEwOKaTDw0Vl7Epn5Cz1jucXHHUo7l1ilMLMydO9dY9omtJsb5a1/7mgGvTAYw6TB16lQTx8vnB4DF0ks/nn/+eSE3MOXoD8Lnw2SCkwX6rbfeiqcwohykZNxDThZoLMIAYMaIbq3ANk19hLjx/i4mZlUnYaIK5K2oCqVA3X54HkSI+dU1D1digVl3ES0U0bjXbJdoh9tUXA/obk1QckpUmSVqVfOp4nQTT7VomwLfRkXJ/u4P3Gi4/98z8TH+mzaibc3S+PivtPVac4W/Lp0oo4pbZPqwOhlX2iwFGhPcrm7RqxpLZNGWobKhpTjek+Z/zJPoZcdJTkHnsfjJLNuIJv39UQIsjRNMyoCVoJ/k9RMKZeluY33suZU4fH4zWzMAd4n1s2E/6NeJvgwkEo4oH0BmdTJofsAWjSYL9u1lNBH1RHQliQY09Aibea+ik9xRJqpd6XcayBoAjOaxwGFpe+yxx4wFD0ZhwB/gCsImLKi4Clu32Ew+LQAcbsJ/+tOfulUjrhTXW66DALCSCVZRrLmAZtIgWYF1GGCECzPu3AjWSyzBgEBiiK2sXNmVBToxDRJEVoks0ADKxDRIuAtjAcciDDi2LNDp9iVVGiTAqM29C5D/4Q9/aFyHTz/9dDMEjmEdxVWdvLwAVT43LKz0mzhbp/WUuGp0YfP90ghkVpBnWVdpJgP4XJ3jxiWZhYkBxkq/APfEWtvPh/jkOXNiliH6RRmEayayQAOQKYNQjsmMRBZoxoHYcmZH/1133XV20+R8ju/00w1PkbKqd4w1sYuA3diMcuIZx74WyqtySSFyKsY6lNK5GW3WB6YuaYm6QOcUu5ajlnu+L5UlQU3JFdYYYJ08UFmvIHf9ip5BLTHsZVIn7S/cJYXHXZSWygdzodwh1WLon9W9ry+SW92/vVf6MqadVeeAI2fI/Jfel2DA4cGhjTdtb5FxE0fsrMsMynZam9qZU00HbpjxUz6gscCudNVArnoRBrZk5jFEHVe6a8AzZJwGnLd2P9HtSEi8IyZ3O+oe2PUayCoAjLoBoYAtC7h29kdA3lwrgCJcdnFDxoqIlRGrIoLVEfCFJLJAk0IoWRokLJdWSOODJKZBsuet63NiGiR73rnGXciZBgkrKYRc9P+qq64yLti2PFZMpLe+pEqDhKszcbFYSQGusDsDam1srE0RBMEXrMuA8DfeeCMOXukTbscIYP/aa6818dKwRkOiRd9xYwa8AtwRPgPA7ymnnGLaJH6XnMZ77LFHnDgLt2xyA/P5YLmlT7hVc32Eflm3bazSThZoQDyA3/adMeJOT19pg7YtC7RtyzQ6QP/l6L0L0O1ui0xzQDoh4fHlpVl48Bbzjt1PwvnqDeFv6Psgw37xjJze9/qDoGZ44zIJLn5epgzTFw1PbRwApzM03PenVCvx05O/EN+sL4h3WE061QZtmXzN0x1Vj5o07BrddMDvga82lk2g20n3gBz+pTnyi2u6T5Cfev5RAju0K8k1sEljeV/9Wyw1YfIS3Y9yL953w6NywKlz9LmNG40rEZ203rQ5KMWqHJs+qjetENK0aYvmndd3GZ77rnRqILL+RfEWt0pYUxX2PDUTlOjGh0V2O7CzsrvVLzTg3tE7+WPAGmuXmTNnZswCDVhCAHKITT2E+zPniAHGBRfrL3G5WFMtKDUV9B+AFoCN67N1hQa8keOW/UShrHX3BjjiooyVEjBI/lwrmfQFF2pih5399zRSAABAAElEQVQLwJAFkigsrTBCW+ZmLLgAW2eKIAipsGADLgGrCC7fkyZNMtvWGn3RRRcZnWMZBoRitcXd3Y4V6zYCUdbcuXPN9owZMwyLttU3OgOwMvFALmb6iRUaPbPQL5sGiXNOFugzzjjDXIs8zgjx1CwXXHCB0SHjglEai7RtyxQcoP9a3l+o9xf3Ufd7Kb0haXqAl/+RXtFBXMpTc7BE+uCSZlVivspD91TX3TJ7KCvX/jf+omzarTKstFVqhjTo71961nP9xsukYfUytKRN67dJYP6jWak/56BbNC96LG2Z82ia2/pz0PhR7Lc2zRpZU2zblgY5bvK3DUOxc3KB7b/+7h/yk0t/nzW6yHSgf735SfGrNRf/qnSfONjYt63fLm8/tTDTyw3a8mtf/0A21OmvnklLmN4wKbt+S1TWvvlhehWypBRhR+HX/p/kjdW70jgcpbozo5I/RV2llz0o0folWaKdgTNMFwB/Bp9VMhboVJe17rG1tbWmCOAWcAZgg4Tp5JNPlnPOOccAX6zZxBYnpkEC7DpZoNNNg2SJsFatWmWInVjffffd8a5m0hfAszMFEtv0FzdzSKDoN21jFQeoYskG3DNW9gG+WIgB0JBZkdMYYWyW7AvyKayvFhDbjgJqmRiwugQAA0KJu7XHbFm7BrAjuHxbwWpMO+iPftFvhG2nJLZpz9s1ZRmTXZzHne0MlO2m115WwGAJSVL98KcaDbmCI9K6eIFke2wRwHVNwz7qAdK3n+FgMFc2lZyWStFZczzw9t80rkCtlvq1/OqcD028b++vynAde+T0OR/E9KQxWoH5j2WNzlINtP75f+jvHOnLUpVIfpzyQWWMblYvGn7nXOmqgR+e/yu1oMWeG2SkVh8as7CNPDrvBVn20Zquldw9o4HXH/2Xcv2FxTId9HR3cS6oCwC4ub5FXn/4bdOG+0+NKU+/JS0NflndVJbW95uv8bKGSmlr9Munf49xlLh6jGkguv6feqO1aM5z5T7ZW589haosry4QirHodo4vKr4pmm0ZTteQWoqX3Oeqr59poG9vXv1sEP29O8lYoLE2zps3zywAQQiciHfFFZj0Q4lpkIg7tSzQgGMYkWGsTpYGCX3AAk36JFyhV69ebWJge0qDZMEvABDQiQt0qjRImfbF+fkQb0vfAfOQQXFdLLzEzGJxRQCIWJ6J1cYyjF6sFRdWZUjEENpJzNOMXsnpi/6wtiIWAOOGblmZibW2xFuUwaqO5RuSKuJ7uTYTDfQFoi7EpjmCddqZBokUVljRrTAmLOm33nprPA0SbN0AZRYLpG359957z/SZfiees2X607p91XL9jScfs7W06Q9+WgIhFgBYC+u/0NbMYpHSusQAK7Ro4TgldStQ74PMO/7p0uGyaWMsHCDz2oOnRrSx8z6qKPLLVSe8poPTOHON700med6QVBW3y/VfelmK83lVjkmkYZPdzNq1f+0a/Y3ySkDBbLo4lnKwQgeCymDOb1zD9qzVX7KBh9TLY/6Li9WNNPXvZHurX1556p1k1bP6GKmiALJW2nTDWoKd2mSbBfDLYmXZwtV2M+vXmxYtYyZeGgKF8qkCW/O9dSqxQ0PcpqSJW9FYLq0hn7lvNy38NOv151RAZON8ZSyNhS5pql/J3125ayZrtgu1COeNjYhvou7voSE5pR0K1pzA0XWvOJtwt/uBBngVdWUnaMBaAWERtsttt91mLJQAXCcLNCCNWfLf//73ZrHxoTAYAxCph6UToAkgPvTQQ42F9KijjjKgDdZgWKCJga2pqTG9t4zI9MNaGmGBph1cobkmzMXsO2fobV8SWaABcBBxUR4rKymHMu1LKrVCboU+AKMwWBMDTQxvdXW1qUKf2KafWGABwOQiRkgJBcB0CmUA+ZZ4Cx1ccskl8SIAYMjOsAJ/+ctfNsdxpQZwA8KtEPuLGzXgFxAM+zSx4kwcIJYFmjZIg8QkAxMFWI+ZnMC125bD6m/HSOwxYBsmcsR5TfZJzYXFnoUx9HcJd6SD0rkBvT8sckvyJO0ykBj49TJLqpKjkxPhhvouJbJxp7XeL0/8baaZFNDbNi3BYrxhQ7m8/tpu0rKx8/5Nq/IgLJTIpl1SEJQbT35R5k5dKWWF7VKYp3FvvoBZl+v+EdNWyhXHviHlhdamFFNK1J8OockgVKBjSJGWGFNxUMGsP6BM7/p1ZUklnAtpOqS29lgMa44+N0ID4Dcs1Xj+HcebG1v1+x2z9KZqH2KsLeu3pTqdtccbtjQqJ5u3y/j51vIGYMEua44Bjjuns3RHpVHruxLTQNu2Tl00B/Plw21DZWtboQTI5a3fYxa26/TYkvr/z957AEhSVfv/p3s6TZ7ZnbCzcWbzAktaokgQJAiiEgxIEAmCIiIP5SlB4QkSjDwFMTzlEX4kwT/KA0FBEGFZ2CXswuac0+Q8HeZ/Prf3ztT0dM90z4I7O11nt6bSrVv3nqququ8953zPaAOUre6cx9ptWT1v6ztYyreQV4OrfeVMahxQ4Aswdkr37nB9OCtylz8wDcRNZB9YddlbEUARSYcF2mktdGqMOrAGAsQAnzYNEkRMqVigAZDONEjUAQCEEIo41UQWaNx5Sc1jhbYAOAGJEHgB7DjWskBTFwzWkEYxpdsWC0bteZgT7wuw5nzk4bXnBsA60/+gg+uuu84canP+2jnkWMT4AlARBgkSWaCJ4XWmGQLcQ7ZFzDDAF8GKDAv4ww8/bAitGEyA2AqrMVZfwDfL5OgtKioS4nwtCzQ6AUxjtYZBG4sw5dmOWBZoZx+pH4s6YsuZFf0DazV1IZ///PB3afWXV5i28seCYCxAA30o4/bsMJJLtw56+EaX9dSTrQu5o4ulZUut/O8fjpETT16osefNev8kR8L6s9EBoRxZumSsvDFvmlF+8USXPdYTyDUxvM57KE9Tcp22/2ozNbYFpaXTLwXBsBSrhTiVeHILU+3Kmu05Rcr/sCvPeoS83jGvBPxqydCBLoODzZ/47z5u9dWBrF2s2yiJFGk+JS90pVcDxaUFvSsploIhv0xwmaD7acer91N7U/zd6NzJbZgIdp377XK0qVndp/X+9bufunmjC6Vh9RarGumK+WRja7FObNr1w97lkt9TaNcCx7ri0EDBOKOxgYe1HOVZDGY3V0eCNobFqvtUGOQyYNWD9ChdcVmg60wO3FQs0MQGA15xbcZ1mNy+b775ptx0003GYorrMq7dgEfcu+3AwhIlZ4FZGZk+fbqZAyphgSaOGIIprLykGoIFmkEEywINgH3yySeNxRcQD9jH4kzdpMVCcFNvbGw0IB33ZuKIrWWedkKOZe+DTFigOdZZF+eybNEsI+QMtmL7a9eH4zxQPVVHi5/rYZKMg2Ab40yL7WsBq2/8YzmxH8R0+Ual/7tKPH6krFcePF12vLdavSB88szTB0nV2AZNObZRxo2PW4MAGYCPLrXGrVtbpgNkk6SlOdd0Pyfol9GzJo0UVQy5H56SKk0bldp6BugdCPjaE3tHjbeL2TsvH6exHht7+g8A7ujEukvoQtyLA5Is7sve33lPcXVPVdLAQvdDr1cj8aVILPmgli0HydOoyhK76s53aeDNu/4oITgj1DKZuXRLsScsK/5vrsz8zNGZHz7CjijPa5ItGoKUnOTOvrP7d5qwpbL8Xutx/xLZtyXqXYX7G9aMtDoP71gsr1XdycNqGdbAYVeGhQZcAJzkMhCfep/G5957773GBRZwlq44c/LaYwBOgDGslbjzIlgZAXtOweJLKh7ODwt0TU1NHxbol19+WaZNm2YAFKRRX/rSl4wLNeRU559/fk9V1I11FddngCCu0BdccEFGLNAAVNx4U7FAD9YW3ILJrewU9ADzM2mNiMcFDGMRxn0YUIjlGZ2Qhxc3Z5uCyFkHyzZPr2WBZpACwioGH2CLxnUZQEseXrbfcsstMnnyZGMR53is2xcrgzZtwBqNcG0Q2Jppl10mJhjrM2Dbxh7TD6zrCHVhHebcqVigbV1cJ9y96e/eLDuaAgqAvQqArftzvDe9YNeOJifvJZbi+jaYIVwpnjJBoQVUOOjMI1s2l5oJwBHU+NSgWi3b2sid3f9RHdGP5sqD4oNB2azJSMFk8cTe7+dylolOzGOgIO6hkclxI63s6rV6TynY8PeENtgeWtBr1/vP0eHWhoCmBQlLjpvmrEdBP7jyXnURj3seQL1mia96CuiCRk7L9y77hRxw+HQZM94dGEQ3uN2u/OsbUhmKyLpW9RxTzWUiPi1eltMh8+56IusBcLSxViqa5ovPO0bTxPV/lwykV583KhWNb0q0qV5yiuJZSgYqP9L3xTq3qPfBEnXN10GZNAEw7m+xUUGJNrykA/8njnQV7TX9G8qw2l7TuUwbipvsl7/8ZZMDljy71jqYaT2J5V0W6I8Zd25YoOfPn2+stYceemgfNQGYX3/9dbMNl+ann366z8RgAPLQQw8ZQM8yrNFYTwGxWG1tqiQnCzTuyQDmRLdjLMwIlmPEAmFbB9sYSMDVGcF13BJUsd0piXXb/XZOWVyk7eTc7qxnb1le8epqadaYv4FcngfqS1SHQze0lEnd8g0DFcuKfTs37JSoYt/+uoR13K9x93lJwS/lo76AbH1/XVboaaBONq1uUf31/U0OVD7pPj2+cXWc1CTp/izYGFYipuXLOyWsVt+hSFQHxda1l8vmt1zCHKu/up2N8ucH/yERdcPlHwA48R/gl22tLe3ywC/+bA/N+vn6VxYaHUzI0wEVk3YvE5V0S6EvKsX+mDRt3C6tO4Y/t0Ymvcu0bMe7r0tJQaeUFyhzccLA9UB1MTBbXtgqJfmd0vnu3IGKZs2+iIJYTZYu0ZpK6U74FkymBF5N0Qk6qOXrUuJPN9NAMh3tqW1De9PtqdZ+COeF2Ag3XBiTiS3F8ks8JmmHsOo5yZSGenqXBbpXc4BRAGqiqy9WVa4FIBNLKozMdsL92TI2E9sLkEZee+01Y+3l2hEH/eijjxq3avQNARWxxZYwi+OJzyYGmBhjWKARa+3FTRrBWr548WKBkIz7YsuWeMwM8cAfFgs04Ju2MgGSh7vUK3BdWTdamSIzf3zQvR2t+dLSFXABsF7o9a8ulnaN681E0CF3SVtnt2x52wUbHRu3anqPXIlFhwaCsVy2NORJx4b4bz2TazGSytau3CRefc4t2qHPTuPinFnvVjWUKCeC/r6XrMvswBFcev4/8UzofU7G1I4J4HX+A/wi4c6IvPDUvBGsjcy6Vqvvma7mNgmo+mYXd6r9N66nwWuhnEf2L9Fj9JHgVVfVuhUbBz9sBJfoWv6udLe1yKE16zVNHNHTg+sSfeOvcHjNOnNs58pFI1hD6Xct1qSGmu5O6c4PSWRWPGwm2fgrGu7OUctv1SjpLot/X0ZbybM+uO7Tb41bcnc0kJkvxO6caZgdSzwpLs7333+/YTemeZAXXXLJJcY9FlA1FLFszBwLkAZ4WUBGTKsVfgSwQCcKLrWwJAMQcXO2zMu4Mn/mM5/pSe0DC/TPfvYz4wZNHZzX5sdlHSsjYA43aVyh7777bsMCTSyrtXZSDqEtiSzQWGSJr0UfpEXCZXiobYmfJf4XkAuwTRQLQGFIdhJYUe6xxx4z7MzWAmuPhZkaId44UQCruD9b5mYszlxvGLpxD8dVHddm4n4RdAAYxv0c0i8E0MtxeAbQLluXZYF+8MEHjYWYfMRYrxNZoAHyNu3SPvvsY1igcetO7CNEZaRAQrhuWJuHs3S1tkukKyhLtpfL/lXb0m5qVD+qu5QwZ9mOcvEqM2/r1tRxm2lXupcXbFULEa59zeriXKRkQ7wbBxpUjrN1eqRV9ah3rTRs2LGXa2D3mx/raJemjgIJKPszUyL75kBn0GxeSpKTI821+eIJNQ9UdMTva9keZ2Wv78iVt7eOkUPHpjcgAFheq+B3raZWgZu3aXPtiNdVuh3cvqVOSHGUrtTtyG4vBKeeGtf1vltGBWJyyKgOebMu1wBhgFky8eozMaiZBg4pbRc8VJFYNCat27I740Bk63qjC0IbPjF7iTz//gzpUKb3SCz54Ctuz4WhDjlm+uoe63tkS7wOU1EW/+kO7+ztfV5QwrMniXdLvXgbdPSPFzQvcH2RdxdoesOxCn4LcnvLdytfebfylnvgVXBlT2sgqwAwTMOwHAOCLHMyYIj0QlgZWQYoDkVqamoMsPnJT37SczgglnQ+p5xyigGS1nJJLCmW0M9+9rM9ZQGlgDbiWlOxQNNOUiDhcku8MOmSElmgq6urTT3URwxuIgs0wBY3ZCu0hToTWaBJ/cOEFRxQNmvWrD4s0IO1JRULNIMMWDqtkGN39erVZmJbojsxFlhIsoi5JVcvoNSKHWygPgYaGBBgYOOGG27oyQ9s44U5HrZl6po6darRzac//eme89EuACt5k4khxnUa6/G6desMAKZdlEGOPPJIEysM6CUXMdbrxLpoE9cRxm2uKwMquHUjiX0khvi4444z+5z3j9kwDP/AqBmRTqlrz5cFG8fJ7KotkqNuVTrYmVJIlVKn6RWWbK/QMhqj7suRQHF+yvLZsiNYmCvALj7ompQIK6S5azX5jPm8cwJhgLH+V+ZOdY02Hy3xD8D80S7hkEefs2hs58ZSqayu1XsrSsjVoILFuKvDL7WbiMnXe3LX73vQA0dogVBh3LOG7tV25Mm8zWNldvl2BRKxJDHBysKr92JMTR8r6kbJhub4oKZHHwK5abAej1AV9utWgf6+SePT1UmynsElN8/lRrBaCiU823Bn/sjoNlnVEpCdXTnCTxy8wcMS4MvCeHWXnqSTz/H792gKqmCWv2u8xaOsWg155Un7LpP1taWyfFu5tCsQ7hWPpovrkhmV22XC6IYeokv2e4v2bu6S3j7u5pIvgRFbyShj1RU6kK3fNprz29yU+n3TR3mOU3q8Lvh1qGOPLvaiiT3ajA/35IAjgA0WV8idEJiEsY5CDgUIPOqoo3pcXIfSGs6RKAAdmIkBZQDM0bvSQwDKSJ9DfmCnAMBI4YNFE0ttTU2N2Q15EoI1NlGwEgPYLJMwZE1YMW0aJIDZSy+9ZNx5sRCvXLnSWEZtPbQFgAt4BOQSr4yL8K233mosorgDYx3Ggomk25ZULNCAxffee8/UhX7IMQxAJL0RksiQDGikfegPSzCWUicIhrwKVmlnbl1ALkCf+F7OhzCwQDmbBglwj0Xbns/ZLizU9D+xXdZtO526IDGDhIvBBvpJW6yF3p7TNEz/YEG2wuDMcBdS93Q2tppmNqsleP7G8TKhuEHGFGospm4Fmln3KRgk27r8slpdphvUsmTFpy+N/AosRtkto6eMlZ3LNholAOLalaAE3QE6+KgDBPORR3xlxPhZod24BHRkubRmeHsL2LZ+mHNfcYl0mfy1Htm2drQUjmqVgtI2vQlhLu5/ZoAv92lLXZ601DMIE9epr7T3I7H/USN/S0GVpi9ipGWXYAl+ZcNEqSpokcr8FinSXMp+HaAh9KFdPRa2thTIppbCPlakgLoFFo7VelwxGph92HTJ4WM4TTngiBlplhz5xYrGlwtM91HH4EGer1tmq2szvAkNXV4zIEiK5aDGCBcpQE6WbhkX9Gx/1/jH1qhLmwKviFogVdBTdVm9mTp04JUJCakXElM/8QfE1NFvR/Zt8IamSKx5gXa891nZo4VBfuueQGVPUXdhz2sgKwDweeedZ+JFAbqQW5FrFQKlRAFo7a44AQwWZyyAgG/S7gDAbcyp09oMMzAWU4Dqiy++aJqQyAJ9xhlnGJddABUAsLq62vThnnvuMaDUyQJNBZwTSyW5dzNhgYb06dprrzXgDeBOPl7rug14XrZsmaTTFgBoMhZogCaAHfB51113GevquHHjzMAE505kSMbNGVAOO3aiAPYB0ABTQCXLWGyx4sMuDXC3gw7PP/+8yfNr0yBhJcZKi36QdNplLfjp1IUFGdCLi70zDRJ1JPYxsV/DfX3yxw+St3+7Ub+V4+iiS0HbqroynRR8BDsloB/JkJZ0aWwrsb7J3KyiHW0y7si+LOjDvd8fRvtmfvJwWf23N5QdvLd2gHBXCte03lL6+o2EpebY/Z2bsnK58OiPSe2jD+zqu7qT1xWYmN5cJX3JLezQHKBYhDX6UoEb7s5tTSHpaA1qSgoHOtZnf9GxJ2Sl/mynSyZWSF5ZsXTsGtxiO/fiZgW5TOlIt3oe1Rx3YDpFs6LM5BnjpWbGOFk4L55ub6BOYyk+/8rTByqSVfuqjz1QXrvzkT4A2CogRz/VRgc1fiENwQJcsV9NGiVHbpHQIceK59G79Z0RB8DOnqYEvY5CnkBIcg85zrElexd9JcdKpO5ZHZVuykwJHr/4So7P7Bi39IeqAccXwId6nj1eOYATN1Qsv5WVH94oDGl87MT5PvnJTxoQiTutTbWDMgDbthzxqLg033zzzT2pdGpq4g9sLK6UBcQDSI855hhjLV20aJGZQ/ZEXlpnXC/l6S+xv2wnpvSKK64w5E7WAprqgtBOmJbtYABWUCuZtAWgT5+cE+APKyix0eecc45JiQQAJf4WSzaDEva8nJP4XIAkhFROdmbbHsAxgwdYzhlcAPBjOYfMjLhr6g0Gg4Y0izlu0IBS4qztAAjgG0mnXenWBZBH78eryzZ6YP3rX/+6uSYsO/to+7I3zafurw9yHYnvLwo+OkNS25Yv29U6hMU3GfiFhXL8uFbxhVxXoCmHTZKcbnWbyli6ZUxRhxSOyW6rJWorPrr/RwXgtq0pV92bS2W75k/eurrCzFlv1zzKfcCv0b1akJLUk/Fl2csPOPDsw8SnA1hDEXWIlgn7lkluScFQDh+Rx/Csv+3335Rg7sDPukDIL1+4/FQ59Bh3UNDeCKOnT5CCyt3zEsKCPPMzH7VVZu08MGm6+CdMGXL//ROniX/i1CEfP5IO9BYeIt7g2My75NVwiIrPZX6ce8SHpoGsAMCQSgF+yOl6+eWXS7VaT3F5huXXkiB9aBrWipOlQQIgwVpspwceeECuv/56Q7QEIRTgGHCHtREgDQgkHpZ2n3DCCYYtmf5gncXN2qYQoh9YROknIPnxxx83XcMVGrffxPhTs3PXH8AvVlNclCG+mjJlirFgvvXWW0Nui7N+lgGfuBhDaPXd737XXAus34BiQLpTVq1aZVYZIADcJgqWZATAa1mg0SfXlPohrcLSDBkWfSNVEkRV6AoGacQC/HTalW5dnIMPHwYM0CWDDnfeeacBwNSxt0thy9sytlxJH5QoYyji1xjN/aevl1hL7VAOH1HHtL31pswep7rMIDVFXAEal19RL50bN4wofQypM7Eujd/lVZZsUCadGtVVWhlzPNH+1pF0jh5JZWbNCksogAtk5rrMUfKhww7a+59vH/T1nLrPRHl2yb0pqw0oSPv0+cfLjf99Wcoy2brj6BsvkGARYQpDE39uUA79+plDO3iEHVV66Q3iyR8aZ0TpJdePMG0MvTseZVkMVt+sFfSGdKVTW2Did/Q9Ew/JS6e8W+bD10BWAGBiLB9++GEDICGOAlABgL7yla8YN11IiACRWAw/DEmWBolzQZJkJwAwbs9YbiGGAsRiMQYwnXzyyYZACddZLJdYhyFfwvILGzRiCaFs+3FTBjjjCr1+/Xqz+T//8z9NvbaMc27B7zvvvGPA73HHHWdYoHElBgxjbR1qW5znYZk+z5w505BVAVwB+riIQxjmFCzRCEReyQR2ZoR4Y6tH5pB6McCAC7oFuAwUQICGXugP9wSu5BZEU89g7Uq3LuKqsczjaQAL9Nlnny24azNwAdBPZLP++c9/LhdeeKGZiN8e7hKr3yRHHrhS8nMBDJn/Zji2pFRzX7bEBzCGe38/zPaF62ql0l8v+5VtT/M03eLzROWESWskN9gtkXpXh7GmBgnkDvVVxv3rkUCeT6INri59XXVyypz39T2U2e+aAZyTDl4ipf507+M0b/cRUmzcpAr5/i+/KsFAsE+PPHrvFRcVyi2/ubLPdnclroGpJx8m00493MQCD0UnH7/jMikktt0VCc48SEq/ckNGmiBzQ9kN9+qxbliDU3He3CkSbooPWHVD0jGARNu6peW9WeIfddIApdxde0IDWREDbBVLLCauqEwAPdxmyQ3LhODi+s1vflOIp50zZ449LK05oBSA6IwBJsYU0i1AHoDIxtJa5mVnxQAjJoi5cJ1FcB/mWNL1QOBEGiTItrDi2jRIWBcpYwXrNkANgJ2YBglX6GQs0ID/ZGmQSEeUyAKdSVtsmxLntBGwSXz0HXfcYSyktMspXJ8FCxbIj370IwNkk1lOnaCfdlEf1xQSrN/97neGGAyGaQSdMgBAGSzsgNR58+b1pC6iDO2yTNLkCcbi7myXTYNk63KyQDvrohykZLaPXA8GI7AIY7VPTIMUCoV6XN/3Bvdoj+oqoHGVnzz2XXn4mSP0YzkeX4kOBxK/kmscPGutTBqrQKPbZZREV3E6Js1HXdxsmHbf25WDNdqdk6DKODFWri8sc8ZsVaZOtdIN/N5NOH5kr3r0pgwGwtKpMedxxXCXDiZxBQaV3Mmj8Vn60BzsgJG/X1UwqrBdvnDMAnn45Tl6f0LCNtDgQrcENK/oR/ZZLePLNIWPq8KU98i6xVs0BlO5iMTJuisaj94lDTuapKS8KOWx2bzjpJ9eIXWao3rLW8s1dCG9G8yvZGzH3XyRzPh03++KbNYjfS/4+Nny5FV/kCPHL1embDI3JNcnwBeyuwXbpsgXjnSBW9L7prtC1t/pk9GndUposnoQ6WPSG4y/d2IRTYOk9oFIY7fU/ikqwcnVSatwN+5ZDWQVAHaq+sADDzRphACWACmA0wsvvGCImSBnghGZuFssc4l5aZ312GWAKZZMC6btduYQNJFb1pIoAb6SCVZfgBegGfdgK7AOY83Ehfmiiy4ym3/6058aCyZtx6JtBUujkwU6MQ1SMhZoGKkT0yDhEkzOWoCokwU63bakSoOERZm4XlyCLUMyoA/yLwv8ac8tt9xi0hVByOW0zOMKzmCDFVsXoBerL3LWWWcZ922WbeoiltEvMcf0B/DKgIjTJRzA/YMf/KCHURprPJ4Clr3Z1mWZp208NdeFc9u6KMdgRiILNBZgxJYzK/oHd3Urxx57rF0ctnPPqPGmbX4FweecNlfmvTtFNmwdpf3SpO/9Ppb1A1nBWlDdKg/ff5WMrdCPZBUInDyFrjuQr6xCb0x9DEcjyrbbKhV5a2VjM6RDBdIWhkDMa0jFSjQn41hl463Ia1WwZlSo6QQ13ccuZvn4luz86y0uVcLnDqMXwGyXMprGsewuRSVVCwzb8XsTfXpiSpZV6lqKvKXKKh7MlwJplXM/Nl8WrJggK7fEPXPCSmrHkA3iy4kYfVeWNMthM9ZJWVGcFd47Os6pYAq5f3o0cPM5v5C5z7zd847q2aEL3o6YfLricvnT1l/JqMp4Oinn/mxfhsX5nKdvl1dufUDeue+vEmnvlBjpZpKIX/Oy+vNz5cQff1WwHrvSXwNdZdPlr4uVg6Zqq0wo1Tz0ioEte7ZdXl9fLO9vGSOhmqn9K3C3GA2EqidLtD5HttwbFX9ZVPJmefV9rD4d+jqP1HdL+/Ju6dyg7xk1cJR+crartWGoAb1U2S1YfWGFZsJV+L777jPpicgLTEwuwPOmm25KW0lYTK0AirAi4oaMJRO333RYoFOlQQIMWoHNGklMg2T3WxboxDRIdr9zDgB1pkHCFRtCLtqPDnDBtoIVExmsLanSINF/Bh0A+TAkE4cNeEXHWMmJfcZyCoAGYAIaif+lPmKRAZWAYwYoEOrCgl1dXW2suljun3zySfn4xz9u6rJpkKwFmX02XhqrsU1JhLUeUA64pk4GRADEsGDTxmnTpvWkVML1HC8CyygN+zTttXVxTgjL0F8iCzRttuVY3itl9AztG0RrapX0xeSjc1ZIU0tI1m0eLet1alcrXDTilVAwLKNKWmRiVa1OdT3AjT7HoprHMX/3CE72St0lNDo4cbJ0q0eHhWqMyE8qbjJTQtF+q9GusASqXMARLOnSARU+iPXjQxXJgAv5abkH+w/I8LGnlg+9b70KgCmPdOvvP1Ci5rksF//s4/S3SQoukTz9/R6932o5dPp62VxbLHXNedLcHtLtXVKU1yETlAegwIRBxJWmmdIlcMhpWa7B/t3/w01PyIuPzDU7SiUoGrlvfu/Y3kKSI7mx+GfYJQdfJ//z9g+ltMIFwf21qOkCrz9fDrjwEzL/nj/JimfnSVdTm8ZU6mCXvmdjkZiUVFfKzDOOloO+fKqCYDefcjIdhts6pWn9Vg3DCsjrayfJvLXdMiq/TYLqxYF0anqzutY8deTgwah36I56CeuAA7HUrvTVQFABcGDseAW6SyS8U0lbX0nOSt6t/DPFxxzf92B3bVhogPecK7s0ADgDzOA2i6Xzi1/8omENzkRBWGPtBBjLlAUaqyQCCEVs6iHcn9lHDDCWUuJXL774YpPKx4JSc4D+AdBaFmjrCo31k9y2TmuqLU9ZCyix0EJShZUSfQAkrWTSFlyHibV1TgDDV199VebOnWtiY6mf8xHbDPgldzGCNd3GyWKVB/ACfhEsvoBTxNaFtZVYXgYfcKl21mXTIAG4SZFE2ijLKO1Mg0SMM+7RV155pbE8wxaNHtCXdTG3ddEPJ6M0MeSUg40aIZ6aiRhz+gjz8ze+8Q1zL3Hd9vY0SO1rNc6PrzeHFBV0yOzpms/5uIVy9knz5fOnviGfPuFtOVrBMS7PFmhwiH6vSIeOkMY62h01ZOfijhf/KbibZep9S/lwLCgNb8zPTsU5et29+mkJjerQLfGbknuNgYRAQF3P1C2aKe6FsGtZt7O/555UIBwq65DYqr84as3ORW/pWGnQNFHO+xFSrMk6iHXI9A3ysQNWyOEz18msidv6gF+0FQl3S7jyoOxUXIpeb99YK4///JmevTrsJ6MV9pYoEB6l8wIdNLDSVNss9938pF1150k0UDSuTI6/9VK57K3fyeTrLpL31GNhxfjJcsYzd8oFL/xMDlPCKxf8JlHcrk2L//gPffbZ/eqmq0C3tjVfNjcWm4nlHvCrxWLqZbT4iZftAe7coQE8EypPOUC38N5J+CCy5fTdUnX8JPGPdr3drEqG07znpzCcGrWn2wKAxPIICRNxo7sryVigU9Vp3WOxRCKAW9qDyzauzbj3Xqhu2YA3XKoBe4lpkAC7ThbodNMgWSIsgCIkUcz/8Ic/9DQ1k7YA/JwpkFimvbg9Ay4PPfTQnnpZADBbyyzu3bY8xFVPP/20icu2B0AShlDegn0YngG2rDvrwsLPQAQ5lgHjJ554Yg+jNMDftoOBCvQ4efJkexozCIAV1wJbC8o5h1PsNbPb7H47ZzsA2U7O7faYvWne+sYr0tEUNEB2SO1WwNfVNUo6FsVJzIZUxwg5qPaFl3SwJ24BSrdLgBOmzraI1L38SrqHjdhysVXPSPH0JnU96/8Rwk+VCW8Fu5yoCI+yF5dMa5LYyqcTd2XdesOqTbJ4XY3E3Z0z6/6yjRNl8xvLMztohJfG8tvV2dezQP0U1O7r1X993yNhLff3h1/T905yS9IIV1VG3fvNLX+U6y++VwfTV8vLLy6W02d+Uxa9sSKjOrKx8PuPvSTh1g4lUrTPSjt3aiO+jTLhlnZZrMe40l8D3TH13Hr7IU39tlV8OqjqNenj0J1mFchRLxrNdlE+sV6CbYslutV9LvbX4J7fknUAGLCCy2wywRXXgrBk+4e6LRkLNMAKd2smQCZxx1iccdUl/VBiGiTiThNZoP/yl78kTYNEOxNZoAdLg2TBr2WBxgU6VRqkTNvi1Bvxz1iyAelOwaoKeRQgE8CJSzqxtLgjYw3HpRihX1VVVWYZFmhAJQRTWGltSiksudRlU1yRGxhmaLajY+KIEQYT7EAD69b6zkADruvMISyzsbuWBRortDOlEgzegGkr6BLLNuzONg0SlmnuPSYLpG157g+s2kyWaMvuG47zWFO9tNeHJNqVY4BYpm1s3p4vsc6wRGpdxtgu9VzATbetsy8xTiqdGvCrO1s71CVN37Xta9elKpo92zvqJVAYkWIFsaLELplK6awG8RcoSGlXcrYsl5aN26WxtVDeXjopbU1Eoh5ZvHqsrFpTKk1rt6R9XDYUXPivZRLuCKfdVQZptq7dkXb5bCzY1NAq99z4SL+u33zJr/ptczf01UDdqs1mA/cZANfbY72MAzcD3nQb+yiD1K7YGF9w//bRQGynvns1dV6ODryOm7ldKqrrZdTYJimtapKyCfrtOGub5JeoZ5IC5ehq11Orj/KGyUpmpodh0uihNuPBBx806XZw6yWuM1EANYAQrK2Uta6yieWSrVsrYDos0IBfgJt15XXWB/kUeYsBiMT3AoghRiIuNhkLNKzRCGRYkDXRDmtpTGSBxpoNGLRt5TjblmQs0BBxYUnGLRwSr0zbQv2JAsDDtRghBpcJgZkZsQzJ6B7CL6zBuExbgaGbNgM4AaII8cHJBMDKuWBZxupKPDcA2JJZWUKxxGMB/wBXBCAOOEcsOD3zzDNNSiXuEeJ5jznmGDNwQv5fWw6rP0CeNEgILuZYmSEWs300O/QPlmcs1AhEasNdiJdUu5o0bSmUUdUNGoOla734P2Xz9T0gLQp+Ix0K9jTmqLs9TpyT8oARvgP3MvuVEYvlSGu7R1MbKXWkfnhYUhKrAgt8ib3u0PgtU0j/RlqyW4dGP7G4ha2oulVjeT3SuEqfJaqnwcSjccBFU5QHYPwuV/xo52CHjPj94ZY2416wecco+edbPjl8v1X6Loop6RUfyH1FH8MS0Vj+VRsqZNnasWZnZ0P8ed63ZPautTZmFubBe621KbNjsk277WrBzCsISVsLYQ+9Uq9M2q4MrIGoDjxbAeDmMBkQbLf2n8M14UoSDXTy7o2/Z9BlqKDLTP1KRsPKCK3PVVeGnQbS+Gwddm0eUoMgOAI8rV27tscCmFgRbrvEeWIFPeqoo0we3sQyqdatRdOmVWL+2GOPyT/+8Q8DwJws0E5robM+6li4cKEBu2y3aZBYdrJAX3rppYbNmHhSLJ9M9A+hDgAu8bRYSmGBBszRFlyhmZwuuLQFQGlZoBkc4Fjafffdd5u6aDuETzYWNp22QApFLLVzAtwBPplDZkWdWNyJ34V0DLHgHFIuwG+iAD6xQCMHHHCAidc9/HDNE7jLoox7NUATIfYWRmmYnQGktBuxcxtnbTY6/jBwcMMNN5gtP/nJT4y1lxULnGkjdSPUDxBn0MG23ZYDeNtrzX1nwa0tZyrQP+SmXrRokZnIHTzcxZsXj3UWJRqqW1MinS0BBjlTWoPZF+nySvPWQiXUALzpa0PzYeaMHv59/TCvhVe9BLz+uD44D5ZgLLudXX51QfWa2GBABjHCuKQCfNt1isdoxVsWrKz4MJu4d9Qd2HU/amuLp7ZI2QF1khNSlmIFuP1Ftafbc3IjMnr/eime3NJbJOim5sotK1H9xL1zahuK5IU39pPVCnBb9Xcb0XuyU70+4nOfbNg2Wl6eP0uWrh1ndOjR4MKC8e792HtDiVRVxwdPndsGWo6EozK6yr0PB9JReVWpjK3ue58FQn6Zc8w+Ax3m7lMNBIt7n5XpKmQox6Rb995czjt6ohJYpjE4EMgVb1n13tzVEdv2rLAAAyoBNYAjLL/nnHNO0guKpZNyMPxS7mIlmcL1FqCZrrgs0HUmB24qFmisqYMxJENqhVUUOfvssw3IZRm3ZNJEAaqxuqZTF5ZzADbWWYAx1n0Ir4hFBuQSF0y8s1OwRv/+9783mxgweOqpp8y9YC3BtC1dFmjco3Gtpj5L8rW3s0AHamaoC3Tc2s0IaOvOfGlvCEkgv0uCBWET/8LAaLcCt0hHjnS1BaSrtRfooViPmowD1dOdas/K5YJ9ZkjD3DccfVedqVWNaTDx6gBLyWGHDlZsxO/3jJoh3W29bqN5Yzokt6JD2rerlWhrrsax+ZTMRWMuAzF1dQ5LXmV8f6LXgqcsziw/4hU2QAfLDpgmsa64RZ1iDMa8v3qCmXK8UcPsTpqpsLLFJoq/IE8qD3F16NTLRz41R/711AJprk/PU6Ni/CgZPcYFwE4dJi4zqPzbf9wkHyu/SAqK88zugz46U+545OrEou56ggaqjz1Q3nv0xdSj1Qnl8VCq+ZhLbJeoFtY9ecXim3KYRBYPrE8MTr4ZH01WhbttD2ug/1tsDzfowzj9bbfdZix0xGTC2DuQYL0jhRBxwsTYPvzww30ImAY6ln3OnLy2LMAJF2Msl7jgIvwoiPN1Chbft99+28SeYp2sqanpwwL98ssvm3Q8ACjco3F/xo0aciqs21aom5cE/QYAAuovuOCCjFigYU3GapqKBXqwtkBEZWN1bbvQA/GxTJYhmX0wJNMHLKTEOWOBh+QL+exnP9snDzPlsFYj6dS1atWqnrog02KywjnQHwAYCzR9Blhzn+A6zT1AH7gmuFMDhhH6AQs0gss69xQDH5YsK512mYP30j/5R39COpa8rS7MbT09iGGhbMw1U8/GARY8+QXiH18zQIns2FVx6inSvOh9iQ7BldmjA3Ojjv5IdihqgF56Z5wpkS0LxBPtdR0F3AKEmdKR7px88U3/TDpFR3QZf15Ixh61v6z/+5smvYyzs1Hjpp96YIZjKw6e6Twk65ePPO0gKakoShsAX/C9M7NeZ+kooLSsSN7sekSWvr1G+TZyZOZBk9M5LOvLzLnsdFn2JyXC6lS3rDTE5/fKnEtPT6NkdhYJfe42abn1WHUr7H339NGEV9OcXf6AePxuWq4+ehkmK1nhAg25FYCQGNp0hfy3CMfurrgs0B/rYXUGnCN2zrKNWWbO9nHjxrHZuByPHTvWuBbjtkwaJiy5FljbOuycYxLrYhCA2GfcuS0Adg4WEDeNPPvssybnMa7iL774orEOUy9M2BBa0Q5LXuU8H8cmujTb/XZOmcR2sW1vlYITPq0xv/HrOKQ+6FOn9LwrtY6sePwMqKLyU05Uq2TmL0ePkj2VnfgxCY7JbjdylJuz7xeMt8GAih5kJ94K3plnD1IqO3bP+fb54lOX0kxlzrXnK/NpaoCcaX0jobw/4JMfPhXPWjBYfy5U8Hv8544YrJi736GB+fMW6Tt6s2OLuziQBkZPHSsTSpqVYyJZeEjfIylTM6pJRk+Nk4723euuoYGcislS+F/zdUHDk8K9z8yOsEZWh0ok98J7xT/V/U0P17tlxFuAAR6AGFLhkDIoXZk9e7YBY4Cu3ZWBWKCpmzZCMDVv3ry0WKBxyZ4xY4axUH/iE58wbsHE0lowR52wJWOlxZp95JFHCizQL730knH9dQIzyiJYLbFSY30FNBJXS6zx/fffb1iW0cfzzz9vlp0s0Om0JX6G+F/OYxmScTUuLy837sGASCZAJtcJKytglxhmrh+xtqQ0Iq6WOGAknbqox8YEx1ugcTBajxXyKiPoB+syeYKpn3YQG437NHqgzfZesCzQsEizbSAW6FR9dLYBdmr6h1jLt1kZpn+8wZAUHjBdmuYu0Hs3UxDbLTn6nig48vhh2rt/b7O86nEy9fx9ZPFPX9Y46nR1qSm1Yl6pPtd1q+JqeQKF0lA7S4oL3tLvkP5kTYNdUUjcGppmS5UjlniwY0by/lEzq2XqRybLkr8vRruDdlWzWEtlVUCmn33CoGWzscDEGWPltE8eIM//37sS1dszEXqg4bKCgJzyRdebI5P74+AxZ0pHe5d0dnTJVTeeL9fc/OVMDs/Ksh0rl8lMJf0Lt4dlU3Oh4Z1IpgifhjuMLWyWaeOi0rFyheTOdOOrk+mJbd7iSvntqsvEt+41GV9aZwgDN9cXylaZId+7w7Wep9LbcNie7hfXcGjrkNoA2MM1FZCZiVhiI+KG0xFrBYQF2k6QGxFXTJojQB1gFbHMy7jfMtn4UNLz4FLLcZA6ATQt8zIuzyeddJKsWbPGpPQ59dRTBWKl6upqUycs0AjtsNZGXJ+pB0DLObGEsu7UhW1LIgs0gA8WaMrTB+JmM22LaVDCH5iUsYijDxiSifFdvHhxD0iFIdkpEHhxXtoJ4MWSb8GjrQsQbetCPxbwJtYFoCb+2KZBOv74401MMOcj/y/tAtA//vjjhoEaPTFwct5555kmJbJAM8gAYzTu3rhuUz9i2+Xs40DtIg4ZQjCmxDabCofhn2BJjsb7dmXYMn6DHimu1nuwrT7DY0du8fyiNpl1/OpdHRz4OUWuwVBRpxz0uS2SE3FZT+1dUb+uUpq3aHotZYHORGKawmfn8hJp2FieyWEjvmxpqVfGFzTqrxW4luqeJI1KTEpC7VJW6DJoD3RTNG3RtChBj5QHPFKoRvKQfnnl6TRKTRATQh6pKglJ7QbLqzBQTe4+NLB1805prG8x4Jf1X93ZPy0S213pq4Fog7539btmn4qdcsCYbVIY0JzACnb9uyaWi4Idsn/ldplVHv8WizS46eH6arH/2vb1jfLu2nL5v7dnyFPzZ8qbq8bJzh06eNCcwjW6fxXulj2ggRFvAUangJtly5ZJW1ub5OXFSRMG0/Vzzz1nikyYMGGwomY/QBGB/TlRiNnNlAUa8GlZoEmd42Repn4YkgFrlgXanpN2AIIhXCJOFQsqTMa49pICiThWLI5WAJSAS8sC/cQTT5hjAeKcl7osCzQALd22wAKN9dQp6JIYa6ycxCaTZxdwTewtOYcRO5Bw7bXXmoEL0ge98MILBsRjcQWcEpcNCRV1keuXeqxAlDVzZjwOzdbF/JZbbpG///3vtphhcSb22Ar1EvMLszT5ha3QNjtgwPkQ6oMFGrKuVCzQ7LMs0OiXwYrEPtpzXHPNNQZws37VVVfZzcN67imqkLySTiW86pb2xqDqCOAxAPjQvII5yr5bWK5kMNE88RSOHtb9+7c2rrBKcou6ZL+TV8jGhQrkdiqQU5fcbodF2OuLqkeKWoo01+DYfXaopbNUv6BH/VubOZxP5tMBqI1vjJGxc7ZJ8YQW9TJIBdp6exFVZvL6tYWybVGZFH3E1WWvZjT8RD1n8gNhqS6ul9r2PGkJ93rN2HKhnIiU5bVKrqY08+W7+rN6STYvHVsqmxZvlHz9TMgn90yCdMe6pUTZjV1JTwPllaVSXFoozU2t6jkTk5M+dVR6B2Z5qZwSvcd4kahU5LeZqUvfMx27SO1C+lsOaNozp+QUu6RsTn0kWx47SwHvOv1W1N+xlXZNZxbM7//ctPvd+Z7XQFYAYIiUrrvuOrnrrruMVXMwteP++tBDD5liJ5544mDF++x3WaAHZ4HGqouVFBCMyzUxv1h5EcuQjNs2Qgw2FnFA5LRp0wzwXL58udk3atQoczzEYz/+8Y/N4Abs3dYabuvCek1cLxZ4BjYAohs3bhRSLQGMWb733nuNFRfyMfIDA9axzK5fv95Y9NmGOzXyQbNA0y8rFmTb9eE6902eI+F5T0pILT++YERBcEiJNXwKgdU914Bhbfmu7zyPt1sBXoe+DMLm3esJFYg3332p2mubM/4wiQaKNIdgk7qebpSOFr80bSvQ0WNNGdbhM5Z2BhuKdPDAF9xFXhLtEm/VgbaKrJ/nzZwlzW/Nl80LKqVlm7IR76epkFRXXh10cYaa62NErcSaYqozR4HvaGnaVCCeHJ9wvCu9Gshre0MHrKLCkF9VQYv+plskrG73UZ1y9PeMpaiXBkAHtoIbNNdlu7qjp+cx1Xum7Fg68pyjZMXc5dLZmtxSTux0lbpKu5KeBhjof2f7k/KDa34lU2dNkvOU3MmVwTUQmjJdB1b7EmABeAM5Kby59IEZmjpj8IqzvMRFv7pUrpr0NX3fePVZqUZ2HZT57t9vFH8objTJcvUM2+5nBQAGvPzoRz8yIBiwg0UU61wywTqKaytWQICJdVtOVjbZNpcFOs5InYoF2sb24laMlRSgCbjF0opVFVdiwDFAFOBKPmKswBYgY421LtBswxqLlXvq1KnmclxxxRXG0guQpC6szaROqqmpMURXuEdjcWWggphfBju4J2Cgpg2ITYFkVvTPo48+KqeddloPGzV9yGYWaPTi2/c46VbdgnF9ml6msLxNr6PGMCtgi+lHMnGVWIdz/FGdYnbQ2bwcvBMPpgpXdmnAU31MHDzsWg9pqp5QQa9XQ6KieMF6imco2ChI3JW168UfPUZ2/lk9VvSZ0LSxUKcCyRvdIYVjWyVYGFZSp4i5Nzua/NK8uUDa6xiZj4/QeNUrqOSoo7NWd4kdj254Vwo75+nvt0yiyu6OYDQylqEE61D8WI+UVW7TAbEHJXD0pfFN7t8+GvjIOR+VeY+/Lgv/+k6f7Xbl5rm36oBDXNd2mzsfWAN4r1369bP1/ZMVn7EDKyPNvXBOFB9/ktT/+Y96xK4R6gGOLTrhZM1V7x+ghLsLDTx2/cOSqym5cHnGCgzwve3EW+TXO/9HArkuCB6ud8mIjwFG8ZWVlYbMiXhgSI4OOeQQufHGG40r7VtvvSWQVJH6BrKjgw8+2IBfcrfi7stDdnfFZYHuZYGGqMsSXREbjbsyoJYYZwipuEYAY+KVX331VQNWcZNGIJzC5dimjyK1E+KMmwXMIuicunATJ+8z6ZAuvPBCUy/bsOwCpFnmfoAhnGtumaKZc+0///nPm20TJ0407aZu6nWKdbW22+x+O2c7fbSTc7s9Zm+bR1s61eKL63Nvy/mpBPIiCt66jEsvFl/AsVNdlG9vdF+ovVpTi+TG5RJugKfAuTX1MvrsWLpFR5kjqQtl2Z6CAw+SQOUYR7SqR9pqc4178/rXqmT1ixOE+fb3yhT8MvgZ/w2j8uDESZK/3+ws01jq7kZe+714I81Ss+8WVdPAN6VHmWInzNguIX+jRF75TepK3T1y9ZPfkk9fd4YUaVqkQF7ATDOP3UdumX+74CLtSvoaaFOgccVhN8rXDrlRzp10lXzn5NvN+zX9GrK3pD9fn3/mpTzQbzu+L1CYXshg9mpT5IGr75NXH3xFv2vaelygw0rMhopvP/kWHUTsa3HPZl0Nt75nzdAZlj+ALrlzIUJiSiWQI+Gei+XxgxCXBbpXi8QWw2BN3C7AFWIr4oURrLcIFlby8RLfTNy2BbjEAkMshUs7Ql377LOPiYe++uqrTa5erLcATAA0Qhzyr3/9axk9erRxkcadGjC8YMECOeqoo4wLNURXxBJPnz7dlL/vvvsM0Zi1TJMTGCEXMPJBs0CbSveyPx0LX5e2pjwpLGlXtx/SV6XXgdbaPIm1vZde4Swp1fX+KxJp0A+OiF9jL8Mpe20AslrWOzapm2lQ3VE3LBZf9f4py2fTDn7zBUceJ20r4uER6fad27bgiGPSLZ4V5aJL41wJBSUdMuuwdbL0zYk6XNCtnh29FkqPAmOPWoPH1uyU8nGNRi/dzTsk1rhVWVHj+dKzQlkZdvIzN5wld939f2aQt62tUy6/4QwZt8/4DGvJ7uLbV2+XL065uo8S5j+/SL532p3y3YevlDy1xLmSWgOtr/5d/DlhCZu4X4Bu4sub97mGOWiMf8s/n5OKy9JL45X6jCN3T7sOxPzrgX8m7SCW4O2rtsnyV5fJLB3ocmX4aWD3zZvDr08pW3TCCScI8aMQVUGodOCBBxpSKEiyAEMwIcO8DNAaKvgl/tROkDX98Ic/7McCTQOxBg6FBRqLJZKKBdrs1D98EKZigbZl7Jy2JGOB/v73v2/S/xALm4wFOt222PPY+e23397DAg3Ixf0YizxszlaI2cb9nNhcCK8QXKQBs04is1tvvdXUBWkWMd7Nzc3G4gvIRrieuFQDorH+QwYG+EWwMONijQBqcYEGKXBbKAAAQABJREFUhAOGGQRBiB225FnUg5AmCbIwXOXRTTos0JBgpWKn5jodffTRZrJu2OZEw/hPpHabdHeGpWl7oQG/uDynEoAb7tGt9SGJdPqlu0PdhBLikFIdmw3bo+sWKjGYuug2BqRza1BiYQiw9BmhOkN3Zq7rsQ4lK1Hw2x1WF/OWOoluW5MN6km7j1Edde/sinsXoLeBxOhVy3To/RgNu5Z0p6662+KAlm25+V0y+yOrpWpyrRSUtkkor1Pyi9qlsrpWZh26Tiom9JblQ7q7JR5G4qzPXe7VwN+emCs7tzZIq4JfbtHvnn9X7053aUANYEn77eW/k2sPuS5puTf+ulCunvEf8tIfXkq6390Y10DXju3mne2HvE6zCuDFwQCXvmnMMtvi20XCWtaV1BrYsWa7BAbImd7Z2mHI71LX4O7ZkxrIGguwVTLgCZdYpg9SampqDMgivtgKRA2AulNOOUUuueSSHuA2fvx4E3dqLZmUx9qIhRGwhfU5GQs0aZCWLl1q4l5hiMaamcgCXV1dbeqhvqqqqn4s0IC1+fPn2yYKbaHORBZo3IKZiJUFTDMg4GSBHqwtqVigSa+EhZc+EsMLgOXctMsppK5CB1h2n332WWPlPf/883uIqGxZWxd1AGKxtjPAgfsy7NWf+tSnzER58gmTAom2wZLN3KY4wtJMqqLLL7/cDIYQQ3zxxReb+wQ9M3hiCaog6IJBmtzLAG3aQB5h6wpNOct0TUw51xXXetyqEVvOrOifI444ogf8W/I1u2+4zj0hddnVxnmU8Kp+EwROnbvSIu2yBuugMiCDseWIJoWHJCvaFX/cdOu96dHfhit6L2xfI13zn0KTRh3RVp9EW3PEG1TyJp1ECYe6NVVPrENTRynw7RWPdPz5DgnM+YR4fG6MEXpp2lqvz1HVi4JgPu50mMCoSx9fPWKBcUzvWywgzOvXbJPqnhLZvRDTwYBIuxLbOX6ehDFUTmww00DaibQ0S7ipTXLGDVQqu/cFE0hxAMMR1bnPn3WfYhnfCN+e/W2p39wgnTp4kEz4bbfUtchvL/ud7FxfK2d//6xkxbJ629pn/qXvYb3f9FXCcxFLr1cJ7VJJpCMsi+79o8y+/OxURbJ6e07X+8qFAoGr44Hp0EiOkovldGBwOcmx1V0cLhpwn7of0JUAPCUKQIfUOrhbA9Bww0WIUyWWlRhYpwD6SMPzzDPPmJRFNQqqEciaEEB0ohA7i5svjMgIlkbci20aJIDZSy+9JLAjE39LHC1gzwptAeDCiAzIJXZ24cKFBpgS90qOXqzDuBoj6baFPpAP2SmAO+q0Qn9JT2VdoO12O6cPjzzyiGzbts1sevDBBw045bhEwYqPtdiCzaeeesoAWKzUXAdnGiT6O3v2bOMK/fLLL5s4X0AsAJl0SNSDoAvcotEx4JoyCIMUsE7bdjFQAJi3rNOUg8TrW9/6lhls4Pzkk0b/iC1nVvQP8cdWYJjeG2TnlnZNP+GNE+MoiOhoDumkMdUw76prJC9WyLAiyrbbrXOntHfl6LGUS/7ScJYdyct4XrT94RvmQ0RhrnbVIjUFvKo3ptSi+q3fLF2vPyHBj36wg3mpzzl89zSs3iRLnl0gVUpNrLeedIV9ylLMxx0s0Dooo/rlLwCZ+xLgi76jOl/21wUy+cqtUjzJdd3d9NCjkqtWcZ/G8mcq6HjV3Q/Lvr88PNNDs6L8b258TB7/5V+lQLknWpR8MUfvv9JQnpy737fl92/eqpZ113U31Y1w1xf+W7atilsjvfr+ztPfdhuPTIdoavoe+cuP/yLlk8rk2AuP7dmW7Qtb5i6Ul79+m+YA9ktRbvJBhEQdtepg4sq7H5Hyg2bImMNdngSnfmKty6W44zoJhqZLa5Pj5nMU6mzPkanVT0nXunwJTOpNu+ko4i7uQQ1kBQCG8MgJ+jLRN2DGAp90jnOCPnL3YgH8zW9+Y1x8cXm2ABAL5d13322qxAUYEAhQxeUWAUABgAGsgETcbXHvxXoLqKuurjakUbj1AkqxjjqFc2KphGUZF1sIo7C28tGdKLTFunzjZow7MeAN4G5dkTkmk7akYoG258admFzGuCbbHMp2H3POjeUVl2Ssp1/+8peNizJu2YBPZMmSJWYbMbzUBXM3KZVYh9wMMMygA27TuLWjB2KL3377bQOI6TcAHwFUYzXHYo8rNSzhuLDblEq0EVZp5Pnnnzdu2VjEGdi44YYbjMUXXSPcLwBmQO/9999vyLWITQbQ44Fg6zGF99I/r/xxqRzV73vNo4B34EdKVAHIhsZCCb70rkw54eC9tPcfTLMjy1+T6OalQpoorLyZiifcJh1/vEkCh35aPMF+FyPT6vbq8m/+9GFpbPXImELbDQW6Cm4ZpJHUBg5TuLHVKwt+8Zgc/+Ps/kCJtnfIht/fL2UleVJVrbH9yb/prIL7zdt0AKxx5fvSvHipFO4zs9/+bN7w2H8/K3+45ckeFRSYBFNqsaxvk9aGNjm14jJ5eMlPNKa6oqeMuxDXwIrXV8iivy3qo45Cn0e8Uc1Br04yioelUH/m/t68XPoeisj/++7DcthZh0luoXImuCLzb/2tvmdisrWpUPICXerl0f9b0KkmDWHVskXSpd9q82/9nXzyz667vtVPd5fy17x3oRlgPfeajfLL79Tot3X/d/ipF2yV0ZVtEtn6qOQUHyY5JUfYKtz5MNBAX9PMMGjQh9EE3J0BeEOZLEhNt10wFNsJ4EbcJyASsifiUa1ghbTlSJ2ES/PNN99sgBhlAL8I4JayuOoCCgFwpORZtGiRmUMKhdUQ8GaF8oC72267zWwfM2aMAZKAPWu1tGUT57QTSy11IIBIK5m0BaBPn5yTjd0FxAPYicG12+w57ByLNO7FpBuyKauoa968ecZSSzlco6+//nrjpkxdFoAykICr9dixY83AByAWUIreAPjoEEs7OrPHwEyNfPzjHzeWX5jDv/nNbxpwbt22YY2mvcxpFwAXIi7YqxHAN8I5qJs4YvTAOnHMXBOWrW5N4b3wz/bF66R+e7tsaCjWvKD9H/oDdYnyizeWyrsPxwd6Bio70veFF/xFlElM7wftqXkSD/xB4tQHxGNItwYIR1bMde7KuuVoV1jWvjBfXUmV9K4jpMA3PRUwFljbkavHxWTVM3ONV0J6R47MUo1vv0t8huzcVBIfOMigm1EdwNm8WtMmqXfRzhdfzuDIkV90/ovvyc+u+t+UHeU+DKsb9H9dcE+f93jKA7Jsx5/v/LNANpQo+TmafsvvkdEKhgMO8GvLAYLnPvq6Xc3qedPazdK0ZrPRQW1rvjRrBoeBnpPck22dAanTskjjqo3SvH6LWXb/6O9162Mat6gvHJWZc1rkht8tl1GVmv2iICJ5hREpKI7ImZdtltMuiHstSFTDQ9bHDV6u/oaPBrICAFt1Y8WD+OrQQw9NewJE7a4kS4MEQCK/rJ0eeOABA+awjBYVFRlwjGUYayNAmtjVLVu2GBdg4lEBbM8995whisLNmnhUK/STWFZAMi69CK7QuOomxp/aY5gDfnF3fu+99wy505QpU4wFE2vqUNvirN8uA1wBt7iA09dEwZ0Yq/exxx7bZ5cltgIEI/QJUEmfsFgDQHH/xip74YUXGhDMAAGgdufOnUbX6JBYXKzAAFEs6wjWevQG+EeXuIYT/8x2+o71nXbhSYCesCpTJ2Vfe+01U4cdLGA7dTNggC4ZdLjzzjtNW6ljb5c1L7+rOVW75N3NVdKpeUKTOBWk7OK7m8dIe9gv615ZlNQbIeWBI3BH5L0XtFf6paFCzuS42Pmu1X4zdeRVt16PfXIrYVFkcXYDjq1vLVMm8rjnwZa2QumKDm665OOvI+qTrVoe8WrQ6/Z3VvTTdjZtaHj9DYk0t+iAgKaJW1apIJiRmcGFcrVbiqSlIU/j4aJS989XBz8oi0o8cc/zg/YWxtiVi9bLlrUuiZhTWXwnLfnnUuemtJc7Wjpk3hPxb4W0DxqhBTf/c4HEetLxeGTp1kp1ww+aAWzn+5vliP6e69ryZJG+361AWrnpZWJZXUED0drn9NXd60Y+ZlKn/NeDS+X6366Q7/xqhfzw0cVywmd39lFWrH3lrnjhPpvdlT2ogYH9Ffdgwz7IU+OOi0sx4BJgA/nUF77wBcO6+++wxiVLg4QrMkRQVrAmQp4EoLvmmmsMGCP2FMB08sknG9CG6yzWRlygAYNYfu+44w753//9X+Oqa2NMqRNgR3yrdYXGEklqIPrNSyVRLPh95513jOUYnQHciTuG6RhCqKG2JfFcWFaxhKYSCyQTXc/REdZYYp4RADptww0c6znC9cRtGgutFVy6sdxizXfqHM8AG5dN3Via0St6QkfkBSa2ePXq1QbsoiMEdmpYoIlJph8wODNQYdvFPQbwxor81a9+1RxDDDUDFwxaMHiBFdkK9eDOjcBAzXHDWZo21xoiDeWKlL8unS5n7r/YjCYnGYTv6UZXxCtLt5fLyp3xOOqwEpnwQs7JYvKX7tbGHv1gBc4JdCsZtC70DM07AUgcGHsUKCe6pkZrN/bUk40L7TsblMk5PhqvTwBZ1lAmEwsbpMjfZeKA0a0VPvBwjW7sCsqGlt7Bt1gkIm1aTzZLx+atPd2v3666Ub1N3k/zTavOnDrsKaQLESVma9heIBuW9cZPh+vqnUWyfnnp/NVp6YDbdMW762Tc5OH9/E+rMx9QofZGzRjQ8zzMvNK6jfFvhcyPHFlHtG7ZKdGOXsDGj/t9BbhFoQ4pL2iWvGD8+dmq8f87WgqlWT1pnAIxXuvWWuemrF7u7tpl2XVogWdkabl9Dzl29Cz6FDNvVdLK3vdOzy53YY9oICsAMCCR2NG//OUvJq6TeExidWFA/vznP29caIkb3R2pUZdlAKIzBhgmYNLawNQMIAI4IZZ52Xm+9vZ2kxuQPMW4ziK4/HIs1kfiXiFLgmwLiydpkHATxrpIGSvV1XEWaAA2Ma/Uhys04A9XaIBtIgs0FuTENEiUw/qdyAKdSVtsmxLnTvCLW3qiVdqmGyL/LtZUQCFpmIjTBeyyzQpWYlyaAanoA30D8p0CuKV/xx13nPzHf/yH2YX7Obqxwjou6gwiwD5NbDLttCzRtMkCYK4PINnJAo1V2raLspCSAbadLNBYhAHA1O30LIB8C0sxwj0z3CWq6Y+sdCqT7hPv7iNzJmySscXNfDNrjsH4AAvjLGElHAqrRe6dTVXqMh2PoeZYLG6xcJYDYHIdJUiOujYDOEwaJJ1bieddTQFEIl22WFbOuY8SZX1zieRqrsvSULsUKBDOUdIcCK9awurWp27PHdF4uqSe49B5pP/AYM/+LFjo7hlEiHe2fluRLG33y7hpOySvoEPvSyUYUz3aWDfY3XF7ppxT3BRnTm2Q4cHxQ+67q88a7+xIknu5T6EsW4Eh25tj3V0y73zit0XmNYyMIwgTSSZNCnSZ0pFYijrSOXbklRnCu0IRcnd38usw8vSzd/QoKwAwlwILIGCXCUD1pz/9yTAMQ3hE6qJp06YZIIxVcObMmRlfPYAprrKk4EkUWH9JyWPjXbEQJhNccLHmAuIAYVZgHcYVFxfmiy66yGyG9AlLMPGtxBBbWbu2Lwv01772NdM/8t8CDJOxQMNIjdswYBfLJmRNxLaiIyzCThbodNsCqZe1iNq2EW+LBdcKLycYpxNZoG26IdyROT+CZRdQjmXW+VKDbAr3YkA92ykHkZgdRLDnok/Oa7Nq1Sr54he/aFyZGRg4++yzzf2AXpkSBYuttZwDlBNZoJ3tov0MaCSyQGMBRpztZ504ZiuJbt92+3CaF08o1zRGmmRGCTWQLnUlnbt2kiHWGFPYLEXBTgOCYZDc3lIgta3qGmmgcW8vON6XG+jdkIVLnrwS6W5v7tdzvYV1lJjN6X04e8sm9qsjmzbklhX3uEA7+92uILe9tfd549yXuMyATGh0XyCXWGakrwfHVvXrYmtTrixfMFF8gYjJ/+sPRBWkaVqzlqB0KjjWO7XfMf7S3oGufjuzcMP4qZWyfcPg1rMcvQcnTOu1pGehqvp1uWBUgbriDwFs7KqppLK4X53ZuCG/qkw86m3VrQMKQxFvwCd5Y0YP5dAReYzHV6zfP22Z9U1HtT2B8syOcUt/qBrIGgDs1CKWRWJEmbDa2XQ7pMrBpZY4YcAik43fdR4/0DIg0gqgiJhT3JB/9KMfGWCdDgt0qjRIuPJasZZM3KSdaZDsfuv6nJgGye53zgGNzjRIuIvjUkz7AWe4YFvBiokM1pbB0iBhJQVoJmOBtq7PtMPJ3AyLNcDSmUYIIMqAAWzLpHp688035aabbjIWd6y6iJNR2jI3M8CA3mwaJAAuXgBz584V7g+sv9Y6Tcww6zb9Eq7nkFrZur73ve8ZEG/bRfs5J/pLZIGmPbYcy3ujjD90pub8VStaY2uf5rd1BWR1bXovyTEHTDGDFX0qyLIV3+RDJFy7Yfd6HSoU39Q4CdvuVbT3Hl1xwDT1Jhjah53tNbkxK/afalezcl580P6y7f97WvNQ9/1do4yI5vBu3Jk6bKVHYfouKT74wJ5Vd0HDkS4/UZa/tVZaGgf+YB6lYG3GQTWuyhwaYFCg5uDqIcUB+4N+mfOpQxy1Ze9ipaYw8ucGNT3c0J6TvlBQKg+Lf09lrxZ7e55TeqwyO8Ov09/7qLdU3yXArzdQ0Xeju7ZHNTB035I92uwP7uSAFQijYA6GaAqLMAAFgFSjbs2k4slEsMbaCUCVKQs0cacI4A+xqYdwf2YfMcBYN7H+EpeLy6wFpeYA/QOgtSzQ1hUa63I6aZCw0AIAsVIC1iGQspJJW7DWMpjgnCywHYwFmvMj6N/J3IwLM/2pqopbKmB+BrASZ0tbaTtx0ribM6iBYN2mDdTlZG7mmiMAZitY8YnnxVpOyiosuABfJuJybbww/XDWde6555p2WYCMqzTTV77ylR4W6G984xuGNZrrtrenQRp78FTx7kbsri/ok/3OOtqqPWvn/oNPU9eU3bRQqAHON/OjWatDOh4sypfy2ZOHrgN9Xo45ZKb489JzBRz6iYb3kSWHztHYhP4W3Uxa7SsskLITjs3kkBFf9mNnHS7TD6pWK3rOgH39waNXDbg/W3ee9LWTJLcoN+Pu+0N+OfJzR2R83Eg8YNQ+k8VfMPRUef7CfBm975SRqJoh9clXdZ6SdmSgT09A/OMvHdK53IM+PA1kPQB2qhYrICAGZmKACmALC+XuirUiEw86mFj3WAAbArgF0GKVhuH4rLPOMpZrgC8u1YC9xDRIiSzQ6aZBArTh7kxMKkCQOUzNVjJpC4DUmQKJZesCPhgLNNeAQQSugVNsnOzhhx9uNgOkie+F1dspgG/Lis31S6Z3JwkVx+I2TRyxjc1FF5b5GrdkroFNlcSyU+w1s9vsfjtnO/eSnZzb7TF70zwn4JfZpx+qOknPRTexb16Jyb5nHZO4OevW/Qd/Urx5u+F2q7dh4CPniLcwTiyWdQp0dPjI735JAkUZfJA4jg2oN8OR37nAsSU7F3FdHvOZ03e53w9NB6HxY6X0yPjzeWg1jLyjcjTc42d//a54NMa875sj3lc+wq758fkydXZ2hzKkuvIHn36wVE6pzCwWWBV93JePlVHjRqWqNqu2e9UActC3viQA2UwlR62/B3/7S5p5wIULVnfeXDW4TFCCUwW2g4rHLzmjTxJfuQ54uzKsNNAXYQyrpv17GoMbLLGuWP0AQQBLACSAjXhhAOfuSjIWaNxj77vvPlM1wAgCLYiUmMNujPuuBWCkQSLuNJEFGlIviLWIGwbwDcQCbV2hAY3JAJgFv5YFGpB56aWXGhdezj979uyelEyZtiVRf4OxQFOe+FzA+L333mvAP6B5/fr1Jn8v+kGIpcYqzvVyChZaXKwBrKQtQoj5JeUUhFkLFiwQ0k4hEJIhDCQguG7TP2KiAeAAbMvkbNmpiTsmDRL3BlZx6sLibgVdYo3++c9/bgZTysvLBeI1gDJTIgs0aaaoE+HYvUGqakZJyBuVdpNyJtlnXepeVOZ3iU/d07Jd+KAIfeJ8aft/P8zEk2qX2nTwQQdigqd8LdvVaPo/Zs5M2feLJ8u7f3haYg6StnSUM/uiT6oF2bVuoKuJX7lQdvz5Ielq5Ded2e+a46d9+0JmriRooHZ9rUxS8FHf1Cbt+r6PGFYE/f1quTzNabbpnXUJR7irVgM+9Ta69i/flq+Nv0JBmBIJDUIqhuV3wr7j5dw7z7VVuHPVwNSzTpBNL70pa/78kq6l+9vulppPHi1Tzjje1WGCBvxj1Qqsv+XwhrtNPHCCXSRe2lsg3uI5Eph2S8LR7upw0EBWAmALesmR+8ILLxjQC4DB6gnohRAJ0JKJWCtgOizQgF9AL262iQL51O23394nDRIWSBiFk7FAWyZjyLAAwLTDWhoTWaBtGiTbVs5t2wLYJG4VASBiRcUNHFdhYlwh8QKcZ9IWU1mSP7gUW7Fttet2fsQRRxgXZGKqIcNCGACw6Y5YB+TiLs1AAdZq1hGbWxjrr90GoHemQTrggAMMOMZFGlmr5GEIfYRZG1BNWiRYryHPQg+2rjPPPLMnDRLu8oBqBiCcLNBY/bFkW/BMPDHu8Ay20C5raeacxGv/4x//YDHp4ITZMcz+dNbWy5hQm6xrVZIS07bBX6j66SJVoVbJ9QVMzObuuFEPM3UMuTk5YyrFP9kn4RWRDOoA/IrkHpan5E9x7Wdw8Igt+pEbvixr/vWeNLy3Iq0+RlWNVR89WI74tn7IuGI04Nfn6T7n1so79/D+UwWl+aHsUeb3mee2SX61S4CV7FZq2Naog34+CSnYDSV5VO5QgOxKag0UVxTL7xv+R24+5mbZoPmSk2NgTRGnAPmoc46Si391UerKsnjPEf/1Vdn8zAvSGcFokORGdOiG93XQH5XDbrzEsdVddGrAP+589cDaT5Y8dpVUTWrSwRmPflNrJImmK2zcqXHTR3xb8iaf4TzEXR5GGsgaAAzoBSRa0AtowhJ65JFHGtBLbmAbWzqU62OtkE6mYVsPIMnJAu20FtoyzKkDVmTALuDTpkECNKVigSYFkjMNEnUAcImBJU41kQUad2inSzBtAQSnYoGmLtoO4RNTum1JlwUai3syizTnxRpNv61wzWDBxvpKP2BbhtGba2dd1QHJWGUR6rCM0pRnUMNaWrHQcl7KIFi5sUxj/cUiy3YmYn8feeQRY5G2dbEdEA8hFwCaOgHyti7LAg2AtvoFYFvLtS1nTqx/YLG2/WSQY2+QvLEV6irplUn5LbKzMygtkYD5XO7/UtWPEt2T4+2WimC7BPVD2RvUfNe7EUO8N+gn3TZ6QqPFN0rZIadHpWuVehGAZzXdTEpRPapHlQRn6DGi3gK5LqmG1VVnU6usmr9OuroDUh4kB3DykFaAr/5cZXtHQKLvbzJkbqHizF0D7XlH2jxUmSdzrlor790/TsItpCvr9W5J7KtHB2DIXz3tU9uluDognlBmA8eJ9Y3U9Yqacol0Jh/kwqo5cd9xI7XrH1i/gnlB+er9X5MfKaho6fJIl4INKyyFFHQUFvvl0l+7gM3qJXHe8fsrZVRum7Tos69VU8KRnYFhrt73NltY65Z8TR9XEOqSjoevl8BXf2lKuX/6ayCn6CCZ99In5K3nFkrR6LD4/DFpqvdLS4NPftPwqf4HuFuGjQayAgDD2IuVDQCFEDOKpReASGqeD1JcFug6kwN3d1iguR7k/cUl3ckCDZkVVljL3EyuXUAy8cKwQRNjDBM0Ax0IAw92YGIw5mYssrigM1CCSzOu1bgtWzIt4oOtm/RgdeGCbVmgk9VFu5xiLdZss+117h+Oy4VTJmp6CgW3+rYsD3VKaaxLmiN+adWJfKsADK/GCIdyopLv03j1HAY64j3x6SCFK3ENRJsV8Ub1pancd97ZUQlv8kq0TvehKwOGdy3rukcH7X1jYuKr0I8UXY616+BNV6eC4DTikOKnG9F//3r13aoKj2xp8kudMpKXBrqk2B+RgA4acC/G9L7ko7lRGY3r9OOPL79RDU3yt//8tZx+bzw/+IhWUJqdi0YqlBBsmxxw8UbZsahANs8rkUh7HARj4fAwCLMLE5fv3yRVhzVJoCCq96Nuzx+f5lmyq9hojUU96BMHyNw/vtHPhReX3tP/49TsUsgQettU2yxX7vsdKVOuuso8PN3iPgpUxXsIWd8YlasPul7ufP0mgQXalV4NdM5/RsJLX5PC/CJ9d3vUEyuiz8McTWOoRpNdg67kSw/oOzug4U1wfBQWtEn4nb9J51vPS/Dgk3orc5d6NND+7C/l9ILfy7yOQ6Rus8+kgIzoc/L6T70mLXeeLkXfedqNn+7R1vBayAoA/PbbbxvwC0DCDXXy5MnGXfWee+4Z9GrAgmzztw5aWAsAxhIFQISLMdZFYlsRrI82TY8tj8WXtkJ2BQt0TU1NHxZogB/5igFQpD/C/Rk3asipzj//fFuNqRvL42233WasyYDICy64ICMWaFIL4cabigV6sLbgQp1oUUcPCHHIP/zhD40LMtfEkkvZDmA9JvaXgQpYoBHcjE855RR56qmnBDIsBjBwV8aaipV76tSpptwVV1wh5CrGCos12BJvcW4GPBDczGFuZrDCMjeT/xhGadJV2VRVMDcDxLHyQpqVyAKdqi7ieJksCzTlqIvrxCAM7drbZeOiDfoBAiqLjx/79KMYwME0kOC6tm1Ti2I+jbV3rcDS8dR/i08/6LwaFeDVgMDgZA2PqFFw26wfd2EdSFCjERZfb0jBhRKh2kEEtkd2KAp+6T6NI75yIJVnxb6dS9fLun8uxO1DSlVfdWG/8UzAOyGVlKmFIxaOypoX3pL61ZuldPLYVEWzZnt3JCyNr+6U4gN1MC7YLZUHN0vFQc3SUeeXjnp11W/1af5u/UBWwJtf1dlzP8b0fmxd6RPvxjUSGK83sCv9NPDNh66QjUs3y3qenTw6FbWFNDXND166Uaqmuvl/+ynMsSGquYBvPvlOVZrIzo4cAaiV6P1pXaF5C21q8eoAbLdsXr5F/v4/L8snvvZxRw3uYtvjtyvJSKuGX0WlpTWkj8oc9ciKmim5djwytqpeutuUS+WxH7oAOImSOuc+Lu1//C8BSN3+uRdkyeYyM5gwtaJOQuo+Hl21QNoevVHyz7k1ydHupj2tgawAwFbJECJBdpWJ4MaaCQBOVvcHxQKNZRPiJYAl1lHAnWWBBtBZ12qsiMSsktsXl2/AIsAQK+lAFkZAm5MFGtdqWKAvueQS0y0nC/RgbQFEWiCZqBPLAg0oZAJsOwWXbEBwIlOzZYGu0YEBBFCP1Rb3ZwYGEIixEHTOIIMF14lu1oluyABrxIJcljnG6qu6ujrtuuy57Jy6bKwzc+d29u2NsuRPr6obmqadCnTqx0h6PWDEnpHm9qhftr6zUsZpPuFsllhrg0Q3LZFuv36IzFRr7q6nMSA3x5BDxwcXkumoW4tHtqrV7c3/zwXAqqBVf5sv4Y744EtpQN3JY15pTkHQhntfiVqGS7QcElHSrFV/WyCHXOYC4M5li6RjQ47k1+SIt0ytQGrp5X7MVdc+plRCmeb3gpIzVwcfP3tpqmJZvT0SicoT77wphb6g+CMafqMuHi1dYXl70XKpOXBSVutmsM4vm7tCtq3Z3mPy3dauXhydeBhpdgU9uE31iYcH0tnWJQ/d8Lgc/+VjJOh6xxidRHduUM+iLWa5oKBDJk7YIWvWDjzoUlO9VY0t6mGkEqvdKNHaTcpm7LrqG4Xon+6udml75Ea7auazxu7ss84N2/XaYxI84mzx1fQ3jiUUdlf/zRrICgB8zTXXGIvfUHSbmGJnKHW4LNB9tTYYCzSuzSeddJKgt4cffti4qQNQYXIG9ENChWDJ3WeffeSnP/2pXH311QJLM27LAEwIv5B0mZutWzJ1Xaz5laurq421mRhjhMGFdOtiICETFmhAOrHQCOB/uAsgvn7VJglr3G9QXaVwceYDeCAB/FJme5vGWka7ZNvC1S4A3rpC46iVEKzFI+GNHvFP1MGR1OGWfdTb8Z5af9WNLbZtdZ/t2bqyYe77EuuK/4bQQWUoLKFwTOrDvp4PY7bjCl2q4LdYR+etRBUAc/whl51uN2XtvHPFexLT51ftC3ky9pxmddfV3+0g9ySDMVv/VKj3cUTaF74hJS4ATnr//Or2RyRHeROaI3FQYQvdfPU9curZxxhrsN3mzvtqYOHf35e2xvY+G8PqZsqUTDyaemrlm6tl32Oye5DV6iay7n39HcPGEZeyshYd3N8i69aX6zcHxE1xPXoJF9Fp4sQdMqo0TipqjvD6JLLuPRcAW4XqPLLyDSwbji3JF7s7WqTrrf9zAXBy9ezRrVkBgC1g+ndo2safci5IktasWWOAHJZaUhZZAUQMhQXa5sY99dRTjYU3kQXa1g8ITMUCnQiyaEsyFujvf//7xvpL7C2gMJEFOt222DbZuZMF2m5LnH/rW98yLMywatM+BPB711139XEhvvXWW+Wmm26Sa6+9tqcK3MxnzJhh1pMxN3N+GL+dzM0UJh4cJmfANEI5gPjzzz9vrMzp1kW5RBZogHoqFuirrrqqDwt0ouu4acww+tNe16zswwrAVOq7chVgdEiBX0nldD0ZEMZNjdH57e15avPQUgpUmre4rKex+i16b8cHPCJblWyoIybBqbqO1S0J6ABo4PrcuUx1r+7RRiCxa2vUfMLF8fUs/duytb5fzwG5Rb6oRPTeg/gKTwWfAuBk92jLFgKvXYnsUCuRukHHIl7Z+ECRlJ/cKr7iqLpD99eNhv3roINHal/Ml6i6nyLRndv6F3S3GA288coiiWou4ETRqGpZu3KTzJw9OXGXu75LA9vX79S41f66S6WgmFrbYd52Ja6BWMM2fXf09eAoVYBbUtKqIV4h6ejUuBF9RoZ04BALceIzsjui4SJahyu9GojVbVKdxr2OercmWYqpK/T2tUl2uJv2tAayAgD/O5Rco265uOL+5Cc/6Tkd7rMwDxO7ihuxjUcl9yyuurAXWwGUYmEkfjUVCzRgbOnSpSbulXjhX/ziF4YB2skCXV1dbeqhPoBUIgs0sbnE4FqhLdSZyAJ9yCGHCBNxsoBp0vg4WaAHa0s6LNCkGKK91s3Ytok5+XevvPJK+cIXvmDy/27dutVYZLHsAoptGiFie9EX7YeNGasxTNxYbGGvtszNsH3jbg3oBRxzHLmRrSs05bACQ5bGoAU5h+kzccEAYMplUhcWXdoF4zbXFZbpp59+2nTRntP2l0GMk08+2awStz3cJVSSr6Q4vVaMxnBI2tStucjfaUivaD9DFkC0aLdXWtQK12pYotkSB8m5LuuuEgaVog2jE/7EGrzSscgjvrHKrFuqQI0xBr75FFt0q7ojOzwS2a4ravntEf2o8eQaf+meTdm4kIrFWR9d4lfQq593A0qopC8x3YCFR/BOb7HekyiNQUe9z3Y8my+hcUpiN7VLghXqEu1Xl1N1Nw3rvdq+xi9tq5VJVteteAuzeyDG6iFx3trYJtuXqwtvEqmvbZINC10AnEQ1PZuKywp7ltNZwNqZV6ykCa4YDXgL9HftjQ9aO1XCT72wsMNMzu39lvVYr3lf9duTtRs8Ib0n9T5LR4z+0ynolvm3asAFwB+QugFNiQLQAbjBCAxAs/GlgF+IlYivdQqETOSnhUEZ4FejoBoh9haxsbhmZdcfyLBgLsZtGCHdDiRaNg0SIO+ll14y7MjkCSZFELHQVmgLAJd4YQAflktSMWFZJYZ38eLFxjqMBRNJty0DsUADVEn9AxBHR5yfmObjjz/eNstYfWm7s61256OPPtpjpbXbIDaDOMuCTVymsVpb4i0GFoiBJscvArjHom1dnylHv6+//npDMGbrtdeAchaop1MXJGZYsW0fAfToH7HntOcAnFv5+c9/bheH7bxbXSR90qUxbL0v1LCySdZ25mmb44y7vBYMG7QD4NkO5Xg0FdLWlXY1a+feUo05TXB57+5UcLEmRydVi8a3efQJ3c3AfSpXv4JR5veTtUrc1fHcUbs3CJBX5gI3VJlTViVRjZ/O8exyEVfrecdGJcDSaTAxjjqlA8cVDlbHSNwfVWvkOSVfUffnXrd7Zz+LJCi/OO/XkusPyEc/d7hzl7u8SwMzj5oueb95sZ8bdCoFRZRkceohk1Ptzrrt3lH6rkkCgNNWBADYjf/toy7vJP0+jzrcxPvsdawEvOKZwLeRK8NNAy4A/oCvCNZJK+R2xQKIVfHGG280Ls+WGAoL5d13322KwgyMxRSgChBEElmgzzjjDMFiCqDC9bi6uloOO+wwgckaUOpkgeZ4zgmwwq03ExZo3H5xJwa8Adyvu+66HtdtwPOyZcsknbYMxAINEIX46v777zfA/M033zRuzLiJw4zN4ABWV8Dv2Wef3cPwjFWbGF2by3fJkiWmnjlz5pjtb7zxhowbN05YJ5cvYJj8vwj1kQ4LKzaDETfccIOJu7VpsADAgGOuA+0D8D/33HPG5RoiNCejdDp10UZAL310plTCC2BvZ4He+eQjUlHYJhvqsJrpEHIfiZORDOyspnmBNSVDLHy15gMe/MO6T/UjaCVnzBQJq2U3pQbUAofbcyrBtTxaul+q3Vm1vX6duvhpjxPvxnSUwHF1a7ekU3TEl9m5Tj/oQLJDUCTAeevKNtFPbVd2aYB31o0n3q4hDR4JRHJkhoyWZVKrTh1QsYkUK/idqH+Ruy/7Hxk9vlRmfWS6WXf/9GrgwBP325XWqG8ccG+J3iV0vd+xM6VwlOvVYbXim3KQxJRrJD17pT2qdx7r7BLf5AN6N2T5UncsLJ11N4unJk+6V7TEPbVS6UTjb6Kl/1L9b9BMDxNSlXK37wENDPX3sAeaunecEgBnJ9xeifsEREKMRBokK1g9bTlSJ+HSfPPNN/ek5ampqTFFLfPyeeedZwAp6YBIDbRo0SIztyzQvGitUDcAG3datgMCYYHGmmstoLZs4px2PvTQQz1WJUCklUzaAtCnT84J8Pfqq68at+KvfvWrxsIMWRSAkH7YnLsMHGCRRnATp79MAElci6dPj38gwCaNxfacc84x9Vowy0AC9eImDZM052VOGiRAKeRZDB4gAGaEY9EVLNuHH364if8ldpzjyNOLTtOti0EE6sKijR5YB3xzTVimrr1ZGl54TiYV1kqOt/eeS7c/Co9lTEGzxmJGpG3xonQPG5HlYmql2LShWL0ghnY/xKJKzLbGHVnuam2XWk1jtDuyY+kGCTvc+nenrr352HXPvCa1TRqrn/lPW7rCObJh/gYd2OolI9ubdfFBtH3xv5bL2oXre3L/hnS4a3+plJlSJvtKuUySEoXC8d9/a0Ob3P/dxz6I0464OoJ5Qbns7i+l1S/yKn/llxemVTZrCqlOGuvyh/S7hiCLY5XII2vUNVhHI00va+hHo/hOqtDAafWES/YKZ5taf/2XTtIBsE7p2vHgYNW6+//NGnAB8L9B4cnSIAGQcOW10wMPPGDAHARKAC7AMZZhrI0A6Q0bNsiWLVvkqKOOMmmZYA7GQgmxFm7WxLZawVWXNEiAZNIgIbgT4/abGH9qj2Fu0yCRbgjiqylTphgLJtbUobbFWT/LWLABoYns2liMbR9w58alGSG+GTdtyMWIecaNHAs0Qp8AlfQJizVgFvdvLLwXXnihAcG4OWNJpm+//vWvDckV53nttddMHRbg48KOZf2FF14weYYZKLjjjjuM2zOu1Ui6dUGkBchlwABdUhcu3/8/e28CH2lVpf+fSq3Zt06n904aegPabjaBRqDZYRRR1D+IDiAOiKMCgo4/l0FmhtFRFGEcGZcZBEVxQ2VRcBmgVZZmpxt6pfemt3T2pCq1pf7neys3eVOpJFVNd5PkrZPPm3e7733vPfVuzz3nPIe2Usd4lpTqOrp9q5Qok+6synZ1lczvS1kdpOXw2hYlbgorAH51PKviTbe9Y816aW6vl1jUZ4xu+VSIkW7fvnLZ+4oScRjf03yOnlhld764QbwBZXvWbu3PJxrHcfzuVzZOLMXk2RuIg9rXbJTdLVWS0BRS+V5W25pq1aPDJ22vbcjzzBO3+F9//ox0tQx2kwTw+jV8RJ1Kh3T89ec3CfHCBRmqgQXL5mu6uCqzI538aHAZzaCuYTkpedvVx8mkmemQsMEl3LvWrcaP7h7NUateCPnc15RNKQDujk6S7rVr3KvAjJ4nOv6iAwKdytOh97IC3KJj1IsjpPeznXB7nlsq/stmigeArFdmsvPJjFoKq2+1Bgou0IfgF8iWBomPVkiSrAAKIVkCJJG2CRBLfC+ACYIkCJSwlGK5BKhB5PTggw8akHbPPfcYgGhjTKkTkLh8+fJ+V2gskRBIEVsM+M4UC35ffvllYzkGkALciTt2skDvT1uc5wLMYsm18bR2Hy7IgH+APZZWyMMA9ujI6okYaVzJrQsxAJ224QaO9RwBeGLtxtqLWIDLQMH9998v9957r7HCwgLN4ALx0wguy4sWLTJgGes0guUYCzRtQXKtiz5i0a6vrxdbFy7V5JNm0ML20VSq/3C5fuGFF8wq7eG4sSqJ9jaNS1XApg2cq0C2KxqQpnAurmbq9qxg+R2zt0nAq9efMnrG97jb7TTW2q6DNyKrX5spxx6/yXyY5OIcgMU4rta2Ta9PEW9xXGOENQ1VYFhH6rF6KR2wdoX3tavVMe0rbsBsjjVzDdsnYVKPDysZkZsl3tFl7u1ExCvrdkyVRY07cromk/qB/PobkyUS1fy2wV51DWxzsxr7+04c6p/uWt6/nstCj+awffR/Hpf33fjOXIq7pszOHXvlwlM+LuEdMXUYL5EKHT4IaO+5hzG0MY/o3dwucfnFj/8of378GfnL6nvNt5Lucr3E9b3dq44Zu7ZPklmHabiIKmy0dw0JClLKAbB902QpKktJQo0LBUlrIBUb8Izkm9N3yiRJnVyrVhJVMpbyCp8+SwcPcKU0XpisD55sKR4Kin1LNFAAwAdI7Y2NjQasOmOAcdcl1hUrJoDIpkGyzMvOU5MyiQlGYEsGhfswxxL7C0h6z3veYxiKsXjaNEhYF7OxQAOwM9Mg4QqdjQUaoJktDRIuxJks0Pm0xdk/uwzIrazU0TIV6iJlEbrDWorApM15N23aZKy2F198sWGzhqkaQA85F9ZySyh22mmnCW7hANhbb73V5AoG5FvhfAg6pY8ATc6PhXnFihXGIsx+jkf//F4QkQH+OR+DCw0NDXLWWWcZgG7rYjDBySjtrItzYsUGuDtZoOkjANj2kboQAL2NVcZNfSyLcd92DKAcPW23bGyulq3tVYY8R2mbhjTfp7mCi31xWTx1twSdRDA6yONq0RcnCDiqgwgrnp4rb1uyRb0WEvrRxudcdkloepr2thJZvw7XfY0j5LegHhdLkV5HuD1aAQrbT49smrElLfg1x+nz0lzbthIXzomdZGAKwQK8ctMMaZiyT0qCGjuoTNo6NtsvfEAT8xtXi9LWvbUG/LIzpSk/3H49ooe4pnr76PzPqHdHvP9aZHsucs9Nv5KGRToods7bcik+4cs0N7XJ2xvfb/pZnixX+sWU7JO0mz1vEO5ney8zj6vOdylgvvjsT8vP//StAghWnfBsIw1SL/frhikyZeY+8WmaOK8SLWYTBlkTMZ/seaNGQZvmD1ZvxEEPgGwHuWhbePNWCWXw/ZnnZ9XwA9G9sR5JdHWLX8PgCjI2NFAAwAfod8CFFjdhUvBkCqy/pOTBoohgIcwmWEUBXICwz3/+8/1FYB0mLhYX5iuvvNJshwwKSzCuwcQQW9mSwQKdmQYpGws0pFO4DQN2sZISi0uc7G9+8xvBIgxwtCzQubZluDRIWLkBmpkMyVhHEeuizTmx+NIOhHWOQ+677z4TU2tW9B8PdyzI9I3zOsWmLgJYYi0GdCOATs5lz4ebNL8h+sJ6DBEY58cyj+s17bN1EcvNgISNp+Z3wapu68q1j7adTnZvAP1YFm95RRp0ORp5WG2rzKjskB0dFbKnq0yiCXVH1ZFjvzcpVaEemVreKbUlYf2Idhyk13qgTuNnXCyBijJNb5S+ppMKOF55qVEm17fJtOkt+mFiMibrxc0ofBpT9PT4ZduWOr3GByzuxFTjdupmKa4pl2RMP9Acwocwlxufd87Lzn7u2bk9JNETk5K69MCc3ea2ub+8VD90lZWtT5LK7L5x52QFwGp1Kw1LWXFUfHpPA3wjOmjT3l0sHWFSzTg0rNdzoOrNMXLb84/n+a++8TvpbO02xiC049DQqN2KhuNy6+Xfk/9dd6uUVrg7xp936mXv/ifjGcc7NuaJSXGqWAcV0qMxab+PAZXiXh73KNCL98qrL6+Xn/zPQ3L5NemQqYFS7lzqjfaYjvfq/btza52UlEalXPMAB4tj/WNWvGuikYB0tJVKpDuo5dNXbq+GkPUXcqf6+nu9875fSrw1LMH6/AZNNWRYNtz0b3LEt77eX1dh4a3VgLu/nA6C7gGRVnhgE7cLIzPWyQULFhhSJPbj6jwcC/RwaZAAZVZuuOEGs5iZBsnutyzQmWmQ7H7nHADpTIMEAzUuxbQfkimbo5ZjrKV2tLYMlwYJoAq7NHVnMiRTv00R5EwN9Otf/7o/Ppj969evp2i/YHFlQADLajbXagqScop8zFdffbWx+qJ7+gnIRWgXuX+xJn/lK18RyyiN5ZhBDSzElEFwPXcySt90000GeNu259pHU9k4+4f7c2iaWnw2DR7EwbJ7WE2rmXLpUpFahUuXHJdL0QlbpmivxkriZ9b3MceHye5dNTpV68CL5l0NxnWQSz/61N05rB8jCR1YyJSSQETzA+8SX93UzF2uWccKTvxqpliQa+eZ+53r5niHFdm5zy3LyaadUqoDVh1xnEuteCSsrs1MuQi/RahjkxY9IpfiE7JMuDMi93/zEZOyB/iQq58L1ylPAyTS2SMP/tef5INfuDC9waX/n/jjs7J5gxKr6XWFRD1Rtf0mNIbar9Bs6LBC2BPWjHHpst1dEfn6P/+PXPD+06VmUjp22KVqlPan/5YeDexXgN7X3SEzscOjHh4ILs8W9JoNjn/tT/1Nqpae7NjivsWkDgRs+8FdUjwzKZVHq+vzwFj0iMrojaWk7TmRtmdfkK5166Vs/rwRyxd2HhoNOJyaDs0JJ/pZsMbaiXQ8+bJAEx+LAM4Qm3oI92f2EQMMKMP6+9GPftSwIltQag7QfwBaADYs0NYVGmCI+3A2whzKkgMYgT0ZF2FGXokbxvXXSj5twdUai6tzAhgSa8wEELUMyddee62xtNInLLMAWsiysLBu3brVxPgeeeSRphm0nxhhK5QjBRTs0tbCbvcxt67SzLEiE89LX63VlXMibEdHAFvqsozSNt6YfbYu+uFklP7Qhz5k9GrBdC59NCcdp//Kp0WU/MF+qu1fJ4rUkhSscffjp+ux30p5serS2CmdevSot0PQWHr37auQjvbSrOC3SD/0qiqi0v3sE86DXbe84ZFnD0if1/9+xQGpZ7xWEnnu/6SupkvZ3YcOJuTSJ49ejzXlXRJe/ttcik/YMq88vlp6+1zJLajNZRAG6GE139Mdlcd/+tSE1VGuHfvJDx7S55+DREyV1OJVEsUsf92ebun0pj28bP145T36wF/tqmvnbX/+k/adqzDblahvINycdcoOftPHtf35j67Vn+142zPPaTqjmHSsFIk1qzZzHDT16Gfm3keVpE2/bff89iFbXWH+FmvA3V+gh0j52Vighzu1daNtbGw0RQC3ANolS5YYhmPS9FxxxRUG+AL4sqVBAqw5WaBzTYNkibAAnZBEMcdyaiWftgBunSmQWKa99AWxc5YBtXZiO67On/70p41LNgD67LPPNjmNKQs4hiHbCqmQIASjnbBnZwpx2AjA3rows07fAP5YyBEIxHAzR1fUhbUeIQ546tSpxvoLeRXibDvr9jdjGbH77Zxttn/MndvZN56EGL+K8rXiVcvk/grgt3Zep6S2LN/fKsb9cYZNe+0rUlvZpddhto+S0bqYkoDGC5foB1/4b+7+MNn4pzSB3GgaG23/63/UIXoXS+SpP0h1SZsETJx+/tckccLTJrVJdPVzQ8Ik3KTWZx56aRCTc/oNlB16WL2g7ZhOTq037WiR9n2DAZ0t74Y5Vt+nl780tKv6CdHkbTJAuLOoUzqKOqTZ2zwE/HJguLtHHr7/iaF1uGhLeIN6zPHdMejqyk8BHMu7P/K6ei25WJofe0KSGjKIq8am21SnGtc1GghOJVKy/uaUxBUw8zs0P/4XF2twbHU9bf4aW22acK3JxgLNw/3uu+82fQUUwfYMkRJz2I0z0yARd4o10skC/dBDDxliLeKGIWQaiQXaukJjMc0GwCz4tSzQ5MK96qqrjJsyaZhgSLYpmfJti/MH5TyA0dtvv90AViyvd911lwGRAElAJhbWZcuWyRNPPGHKwPxMuiiE8jAzW7n++usNq7Ndz5zjuoxA9IWlmXhn4oTb29sNqZgdaMD1uaGhwTA9A46JCaYczM+wciOWBZo6SanEYARWcUi5ANNWcumj04qNuzix2wjtG8ss0BLtkCL16as/cp/sfGWypDQXbV6iH8nBiphU1LdKb8fOvA6dSIV7w/oSVfHrQMLU2jbZ0YTnR3pwyOwY8R+fyR6ZMblF72UlLGpJX+MjHjKBd0ZaNR2F9s8JHvLtLsdHWtwLNtBXsnWvidOfO3O3vKoEWMTx5yNzZ+6RYEDhng46EtvuKS7N5/AJU3bvNr50BwvgFldopkzhugUkZ16/Rd4iad3TLpWTyjMPccV6Z4fGUPe5Pg/pMM89/jSf/Giye0fTaEUm9P5Ea4sBr7wrAGDp6yzXezsNnM2xau2MtzRLscyd0PoaqXM9O3f17wbQvnJVr8y7SflOalLiKx2s02RPSq3Famy5MyXhTf2HSTyDp2ZgT2HpUGugAIAPkMatFTAXFmge6oDe//3f/x1ydkif/uM//sO449o0SBAjwSicjQUa1mgEMiwAMO2w1sZMFmibBsm2leNsW3APJjYXgSkZF2aIuLAkE+MKiRfgPJ+2mMoy/mHBxSKOu7BNEYRLMq7ikG5ZhmRYrrG+QlqFi7MF7ejX6epc3seoRzncnTIFQjEE5u2XXkqPJmMhRwesYyHGDZr60TFu4sQ3o0NALf1H7whtRy666KL+lErE/QKeGYAg/y+Sax9NYf3HAAm/NQLwHsuSiqkOinxSOkmB+sJ9svvVPIisinrFF0jKjON2G+AmPYMJy8Zyvw9021KRbnUjT6eTKiuJGjC7Q9l005/Bg1+kg8+tL1q1oM+e0qzz9KdMKjK2r5nB7T/wa9bd1Hzf7Uf1VttJZe51s6R60oRsoWBC3nb4dnl5w2xVB9eY1VA27Wh6M72vG6buk4rSNMmO0stKqkevSZcC4J6uPj1kqAv3Zia0aTU6kh8N75+IxhO7VbDe+nzZhgzy00g47F4doqkk3xTpV0X6vdsPgtlrr0SWnZI+AMuvAb9mlwI6t+uy7xvQaqpXL611X0pJpdKZVJ+oaYCnqUYVVeEe3fZcSlqfRP+2dN9cVZvS7074VAry1mqg8AscIP0DqpBcWKCd1kLn6alj5cqVBogBPm0aJIDhcCzQpEBypkGy4A4mZ+JUM1mgcfElNY8V2gIIHo4FGqAI+L3gggvMlGtbRmOBDoVCBmBybqyfWL0RzocV9Jvf/KYBytYyCiBFLNGUWdF/WGO//vWvm5hhjgXIPvbYY/2ppCygpE5SEKErSK7e+c53GkIv8itbgIuFd/v27Qb8Uj9to04r1oWabQBvXIMzZlsAAEAASURBVLWplzbQPlvWskCP1EdbJ3MIyKyMdRZoT/kUHXpPf+BVTFMQ59ste1+r077ri1RJnIaTIo0ZLq5WRui37TUvVB1eEE91w3DFJ/x2b02dpDQtgpUyJb1qmNoke1sqpCemAEKtb4y9WyHeF6kojUhdVacOkPV90eg26nKzBMuKJRHpC09QRQxoZnStDGhYP16q3GmxtFoqqpokyebdZjWgKVKWzN0q2/fWSFtnibkee02MYLq0YR9X1/1iZYiepYMxXL9WYJKmLrfKpJk1I3ad6zOXazSpxG6109KcICNWOEF31k+t1e+eoYPa+Xa3rp6BRfdKYJK+HxwpGAygHQSCs+km/fYZAL9aRlf86oHnZgnWT5au1WsHqSCln4htSh/RtiKXu1rf18WhAvgdpMG3bqUAgA+w7gss0C0mB24uLNC4DkPshQs0gB0B4GKJxUUZYPn+979fIMDCwg0xGO7IxAcT94uQjggrL4zSP/jBD+S5556Tm2++2cRG40aOOzUCmRcWbn4fgC1pnQDEa9asMQAYt2UYr+35f/GLXxhLOHViPYZ4zNaVDwv0cH00jRqH/zxq/U14a8SXSA+ilE+OSHHlDmndWimdu8pUtwopnO8BBXJBJWqadHirAuA0SKHbCc1HGKhfNA41cGCa7FErmbd2siR2be+vMKTuo7OmtBjW5y4lwYrFfTqwUKRu0kkJKSN0SSimVt8Mm5GnSIILFvfX4caF2vkzpVtzhVoB1DovQbs9c+4Ev+yrWzg7s4ir1gPzFkt802uqvLT2AMGHTW+SqOYD7QiHpFuvSZbt9VhVFtZrcihA8dXPNAORrlKeo7MLT5wrzzzwosR6hurGUWzUxSIFLZNmjAymR61kHBdgcH7G7CmyYc3W/e4FdRy/1L3vGRQXamjQUKWBgXy2AWzTzz/4V9gyIINA78BmtVrqe6ih0bHFfYsVxx4tLU8+o4PXAwN++WqhdL57Xcjz1dXBLj+8yeZgn3mC1m8ZoJkXWKD3jwV606ZNhikaqyrAmJRMlhUbEAvYRWBrfvrpp40rNaRbxBYTJw0xmAXUxAwjq1atElJHTZ8+3axjpcVCa1mg161bZ1yYL730Ulm8eLFhmoYNG0Bs3ZMLLNBGdbJ7h1qKsPj2iS/YK3XzWmXOadtl1tt3yvSj96ilt0lmHL9b5izbJjN17gS/5jCNJ+rommyrcOW8dOk5yqUxoEerBIBHTUVYptR2yPTJbTK5ptO4lw4BvxwQKJbSE8+wh7pyvuDdS8UfCgzqO1odbXIeECgNyvwLTnJuct1y8QlniaekfEi/ieutq+pSD4VmmT97j8yZvk/Jrtqzgl9lx5OSk84dUoebNhx7zlHiD7x528KSM48cxC3hJh3avv7dRcskmHFv2325zCsqS+Xsd52cS9EJW8ZbVq7Adc6w/TNgWB+Wdj5cwdCcw8TblzZyuDITfXvNKSdLUXDwuyafPqO/unPPzueQQtmDqIECAD6IyrVVF1igTzfu3LmyQOMSTO5frLC4KDMRB4wQO0xsMwKhV3FxsRx//PFm3f4jfpmYXAQwDDjeuHGjAcDEMyObN2827suQeyEWKNuYYbYBwHE9t6mpCizQGtuixCTrni3XNB/ZHx2B0oQBu6WTIhIqjylhVsbwsuo1kfDIlg318sbjWRg+UbxLpOI9l6Ut5m+iv7FevxQvcTdwO/L9pylQGHqd5aNWr5KzLXzPO/I5ZMKVDR1zqiQDQwFwPh1Nik/K3nVZPodMuLKzj5ghsxurtF/7f0369bn5wS9eOOF0k2+Hrrru/5PiklC+h/WXn3dEo5x02pL+dbculCz7O0k6Qhjy1UNSPblKT/+7fA+bcOVLGmZrrO8J6lK+f13zlpXK5AveuX8HF4464BrYz5/xgLdjQlc4Egs0TNCk3bnjjjsE62MuLNCNjY3GBdiyQMNwbAGfVeR73/tegb35+9//vmzbts2k+znuuOP641RtOTt3skCTegirK3G5WFtffPFFQzCVjQU6l7bYczB3MiS/+uqrJtfv1772NdMuYmgBmYBcACnph7DA7tq1y8RGczxA2BJf7dixw4BTG3/NfgRXZYioqIuY4CuvvNKQXQGCyS2M0BeA8cknp0eHp02bJueee64B3bgtUw5CLs5F/DOSyQIN6RV6H4kFerg+mgr7/sE6jYWaKRuRl7PsW70c2bNP21gsm9ZMkXhs/x4f8ahfXl81RTo3Dbj/vtX9eivOn/AUy56uWkk4rOn5tAPXta3ds/M5ZEKWDZSGlDXfL779zF8LUHnbkpD4i4MTUj+5dopnZfPcCyWq4Qn7I0kNf9gm88Rb7e44QXR37jvqJLifgzKakVVml8Zk9lx3e8igx6rqcvnCVz8mZeUlrOYtX/mvG/I+ZiIe0ONVzpJEQJnd8+8d75lIQsMfvJX5HzwBj5jz2evEVz7UcyuXrs79549Jkf/Ne4fkcq5CmdE1sH9fsKPX67oSlgAJlmI7ffvb3zYMygBcLJHnn3++0QsxqFgXYYFmsjGwEDDBAs1xgDrSDVnmZdxwzznnHGO5XLt2rcCSDEFUQ0ODqZMYWYR2UDcTllLq+epXv2riXmGBZp19VmxbMlmgcSeGiIvyWE0fffTRvNtiz+GcW4Zk9AELNDG+q1evNizQlAN0OgU3Z9IxWRdoC0YpQ124KTvFCSBtXWeeeaZ8+ctfNqzSpE1CcIG+7bbb+sE026677jrBzZlBA8jIsP5+5CMfkTlz5rB7CAs0gwwMFGBxxvU6kwXa2UcszjBdI7ZdZkX/8fugB6aWlha7eUzOe5q0fRpXte7lmdK8p2KQK3QuDebS+8vDRwkgOLJn8G+dy/ETqUxkX5tsD8+Uzp6Q3p/59YxY6/W766S5tfAyRXNzlJV8Wnmncr3kp0jKz65qk9m13fn9ABO09J74ZHll5xQBzOYjCS2/r7tEVrUW4tvQW1Vvt5yibuJQ2eUjgF+8Ec49PCHRfa35HDphy1565QXykU++L6/+hXQw63fPfF8WHJV+d+d18AQsHG1ulx1KsAgXluPzb9SeAph1XEy2tVRJpKlwPaKwYH29zL25eFTdOQsU6djq7OvKpOKYmc7NheW3WAMFAHyAfgCAIgILtJ1w4X388cdNrClMyjZ9Ty4s0NRlWaBZdrJAAwhnzpxp4lctCzRMyAjtAAQTA4sFFRZorJC0BQZoJkb6rWSyQEMWxbG0+zvf+Y6py7JAW7bpXNoCCzSxvM4JcArwBFxDdAWZ1JIlS4zV08kCTdvoA+ASYiorWIaxAGPxRqjLKQBSQKgFy3ZQgjL0C4v5pz71KXMI+ZSd+XYZTPjQhz5krM+koSL3MAIb9U9+8hOzbM9HvdYKPRwLNAdYFmiWqd+Cc2e72EfaJfIcM9XUjG3Sk1BdjeYUTIOMZx9bINs31kksOrrFKKluz5HugCx/aJFEe9K/W/Fkd7NzhmorpVdZTldumyFtYWUyzsESzMdLXN3PN+6ZJLvbqpRgzN3Mxdw/iLemVpZM2yMNCma9OVqC/VqusbpVFk/dq8e7l7U4rcH0/4r6SnnljSny5OaZxhKcy8cyFuPNzdXywKsLpKRq/91Vne0Y78vFU+vksPIeeefUFgW0PC9HB8J+LVevRHeXNewxJDvBSe5lgM78/T/3r1fJv3/7BqmsKhsxvrpUGeGnTq+TX/zpDll87ILMaly7HpqkLvn+gKzeXS/huD+nAS4GwSJads2eyeINaKgNdRTEaCA0rUyO+E+fhGaoPWAE5wSAr09tNA3Xe6V6aVAzSBbu6bF0CRXMBwf41yiwQI/OAg0gx3oLCH755ZcNMRWWbgSXZwSLM+mMsGKTZ5ecvbhmcxz5gS+++GLj6kxdCOmObrnlFmPptrG6ti5TQP/BEI01Ppvgpt7a2irf+973jFUZyy+CazQDGpdccslBY4G2FmbOx+DAWJZiUko4/KhWPj1Hdm+rliOO3aZpP3E51we+Pw2QwcmJeBocb1lXLxtXTzWWX/rn0YGa8jn69nCxFNdVi6c3nQpp1fYZUl/ZLo11zSaeldyqjNZbSaouSUMTjgZk495J0hlJj0BX1Y/+YW3rmMjzcAlWS5GjpjRJraaKWqM66kkoi7Z+xJFyywrpe7zqmhryJeTI+iapL+s2ZXrKptoirp6XR7Ypy3OvrN07Wdr0Gjth9g6pKtZ8rHo9ojcr3NvxXq/EFPw+t326vL6vVu/9XqlN7bRFXD2vOuJw8Wrs6nTpkfdOb5Znm8tllw78qdoyYjFTElC9Yvk9prpLjqoMm2eov6JMfCX5WZkmusIvv+Y9cu673yEff++X9XtgtSR6B5iNGdT3pbxy2UffI9d9+fL9dpmeqDqsPGymeCVhSBe3ttRIZSgik8u79B1Dnl+9/vreNbzaScHXq9PezjJp7+Ea9IgnFZeKwwvWS3t9FBXPl8CkZ2T+V3zS/nxKmh/vle4N6eejJmZQ1m21FOsrpWppkUw6o0ifBapg3e0JuPubx+pvrMwLAPgA/xKwP2cKMam4GOPSTJwvwgObND1OweIL0AOIYcVsbGyU119/XWAoJlctwG/u3Ln9zMSXX365caH+3e9+108SZevGsotrLSATEHnZZZfJv//7vw9yf7bnpuzChQvNKgAMt2LIoCDvwnJqJZ+24BaMBdop6IEJK+2HP/xhk0cXSzZu2JwPqyquxFiPv/vd7xpyK/L1IoDg8847Tx544AFj0bYAGJfhhCYVv/322027sQ4Dhm1dHAsgxt2Z1ExYmrHaZgouz4cffrhpM3mC0QF1EScM0zQWXNIhIfSBHMsILutYjhn4wEKNEOfMdPXVV5vj2XbttdeavMNYgenjeJWAfpiVz5kpLa8M5MLb+0a1MJWoxWPytDYJaU5QfyAp4a6gdLSWSEtTuSQz4go9Pq9MP3vpeFXDAWl3ateLMmV6m2x7HTd+j+xpr9SpQioUcNQqMCsOxMXvTUqPpkPqigaltatEwjEdUu4Tv+6fUf2ivmzjOqAwtgdObJsP1vzJv7bIsQrIsP5O1Q87pjZN27Onq1Q6VXfRpE+CCnrLA1GZUq6hE6Fof1Pimr96xRPNMu9L/ZtcuUCqlPIXfq4faunY092d5WrVXSjVxRGZqZb1Gh1YKNNrLqLXY4fqFLfIvapfm6/aryB5Ws9rktizQ3z17v7Qq1t6rBnk40KqDSbk/Gmt0q6Dgdu7g9Kkeb67dDmg+ipXxvdZJXpNkuKsb5yGwcGpZ5zoymtwtE4//8gq6XilU2bFpyqcS5o/hWcK7hRk6N8fvvlXedeFp8viUwrWX6cuy3ytfZ5bXGQeBbYlBtwyEFgc0GsPLwXFaImk11h9GTw0G/oq8eg3Wrm/zVmlq5eLkm+XRM9T4gkVSdWJHp10CEtHD5JhfXwm1AhQrtrzDoxgpxL6lIwfpt/9A4OxrlbgGOl84dc4BD9EgQV6gAV66dKlBpASH4s1dsGCBQaUE+OMWzIDAwBiQHAwOPCxz89krb2NjY3mV6OuSCRiYqixFn/pS+kvWEi/bF0UBFyvWLHC5BD+5Cc/aY7N/Ed6JI4jN7Cti7ZAtIXgOm4ty2x3SqZLs91v55S1cdnMndud9YyX5bqlxzCYOUTCnSHZsm6KrH15lqx6tlEtvtOkaVfVEPDLgXw01yyaN6QON23off0PsuiYLWoxH7Bk8NHRoZa3zU2TZPUbU+UVdY9et2uKvNFSPQj8oicGGWYd1iypXS+5SW1D+trV3CHrNvRIS0RjqR0XZlVxVObXtchxM3bJybO3y3HTd5l1J/ilfJO6n69Z3SHhNnfHAce3bRBvolst43sHEYq16vW4ctdUeeL1OfLw6vnyfxsOk+f0utzTVdYPfrmjJ5WGpa6kWyIv/HXIb+S2DSXqAj3trJPF4xuwMVTqfX5UVVhOn9wuF0xvkXOntsnSSZ0yo2QA/KInLMfzP54eKHeb3kbq757tzXLbx+9SLyJFGCo+BbxBCeif5lTXPytf/4fvZx3ot/vdOI+/+JjMrNunll58EKx41EvGL63hUmnqLpemrnJpjZSYbU7wyzEcG3vhMXug6+eRR5+X5J64Dj4PvHA8akb3lXnEX6VDMg7wi7I8Po+Ef/ZS4bocY1dOAQAfgh+kwAI9oGQspvPnzzdWbsigsJQCdgGyn/jEJ0xB4mAh/CLm+L777jOWcyzosDgTR33RRReZctSFRfzee+8V4n8BzABMLMK2LgoCtomvxYo+nGBRBnhjNad+QCrrAGDqxrp7sFigh2vTmN0eQM+A+v1rIcfF1V3NSca2fzWN76N6tz+jMW2dctj8PeJRa1C+cuKpG6Qo1SOpva/le+iEKv/Gyq3iVWbNx7fNlJhaMPIRXP+Wq/u5T2Pcdr62LZ9DJ1zZ2Gb16lArMLHUDBLkR+DkkdPmbFWyiohE17p7QMZeGEd97ioJapx/vjL/mkulZFp9vodN+PJ/uf9ZfU4OHnzO1unWvR2yadX2bLtcuy26+jmZOalFQurBwWBV7oJ7dEpm6LHR1c/mftgELxl9ablEftxkgK4TBGd2G6twKtorXd/YKb172yXZlDaoZJYrrL81GigA4AOsd9iY7QR4w502kwWaU/Lxvz8s0L6+EeXhWKBtdwBww7FA2zJ2TluysUDDnIxLNLG32Vigc22LPY+dQzJlGZIBubgFQzplc/FS7jOf+YxJPwSjNmzUpBsC/JIuyroQ027yABPr+7Of/cywKLMN92tnXZBXZVqTbVvsvKGhwbA9019+N1zVqeuII47oB9PEHyMA5Pvvv98AY3STCws0LtTDsUDT1xNOOMFMe/futU0as/PunfuMS+n+NFBVKgmNL0qpy268U/2FXCypzl2m98ectEnq6tMx8Lmow+tLykmnr5UpM9QlLaEAuOONXA6bsGXa97RJMp6UDnUPv3/dXNNPpyU4W8fZH1G3/B+ri2+nHterwK99V2u2oq7Zlmxp0o+1HhPre8HCdabfgy1GQ1VBTHXIF5dLFq+SUvNxrZekukAXREkQJ9XIsl99x6hitOuRQjF9Ls687H0y/2OXFNSXRQOP/lS/Ffqsv1l292/qVE+OF55w96BgvzL6FpJt+5QgNSVHz9VBKh3aygUEE9Mf0HfN0qPUM0SPpY6CpDWQ6m7nhpXOf9kuiVfDkopoZpfYwCB2KtErvWF10d8Rk+7/VlK7DvXyKvJKb7u7M1+MtetnwD9nrLVsnLWnsbHRpLeBNdgKjMwAMWJX/+Ef/qGfBZpYUiyLH/jAB2xRY23EwvjnP//ZxO0C+iwL9G9+8xvDAo1VlBRIuNyyj3RJlgXaVgSQox6sl5YFmjbBAg2RE2Dt+eeft8VNXCt1btiwwcSxAuxggSZnMBOxrYBpYoRJQZRrW7DqZqb0Id4XgEkMLbHJO3fuFHIAk3OXdjkFYAsoh4wK8AhZ1hNPPGFAPfHBuCT/8pe/NPvIVQwzNf3mGM6TTWhTZ2en2QWwdQpEV+QbZlCCmGjihInhXbRokbEoA/YtC/RJJ51kYnoB5Viz6c+FF15ofhfqpBxWaNtH2kVO5ocffticMtNletmyZSbemp133323KTOW/wWUeRgX5h6NqyzWGCJArV4io4oFv/FedV5T9mOf5m91s3j8JWYsHt2ddcEqeWlFg2xQorBkQuOJlPAqU7yqa78SjJ2glt/ps/vSZRFTFHA3E3SwREMluLhUAME/eW2BnD5ru9RoLDVMz16HKiHKgrxpn7r1PqFuvF3xNCM5vtPBMpdfj0Htv76zlF1IfPrBe8VxL8sKQ3ClzO8KztCbFa8HvaZkakWnLFWiLAt+2V9U7O7r0eqIOa7QW896v3ge+q3UacgCuYEzjZh8NzMouKK7Ut51/RXOwwvLfRp4+CfL5bUXN6m7s+NmHkY7PAn+49P/K42LZ8rxpw3mWRnmkAm/2RNIP9sAtKcsWierNuuzLxzSWx19Zr68U3r/90qZPj+PanjD3OcoyNYx4ZWVSwd9+t6IRtRjRjlf7m8RT5VXfHND4qnzG3fnVGtCEpui0vtGbKC2VK94giNQRg+ULCwdIg0UAPABUjR5XjMFoAOR0muvvWYAJjlmEcAvACuTkRg328WLFxuyJsAqoBqB5AoBRGcKsbMATZs+B7AIiRYgFqImgBnA8Qc/+IEQMwuRFSRRVmgLAPdb3/qWAbnEK0MIBWEWBFDk6AVUWsCYa1sgnMJ66xTSCVEnVk7SNgHE0RHnJ+72jDPOcBY3y4BL8iPj5gxx1j333GPIwE4++WRDlHXaaacJ+Y3pF6AfeeGFF0x7ISTDakx/b731VjO4YAroP8BpdXW1nHjiieb3gP0Ziy+EVk7BsswEyZYl9WKQ4hvf+IYB7pRloAAWa8s6TZshMcOya/sIoEf/iC1nVvSftQyzzu821qVU00wU+XU0U61uESXLCCjIgMUUyQaE+7CJfkAX6Yde+iPaX1aiA6IDH9Rjvc8Ho32eiukav/tif9VHn7BF5h2xS9a+Ok12bKmVnnBAXf4YahApr4rIrDlNMnfhbhP723+Qv1Q8ZVP6V924UD5Z3UxjA6RWgOAHXj9M6jUmdXZFh0wuCZuBGq7VPZqrdmtHpezVuF/nhx+Wz4rJVW5UX3+fvdWaCooPuwRukoqF9doD3C6Ztlt2tpcbQrEu1W2JLyY1JT0yo7JDKjXOOlPcToCVqY9FZx4tX/3Fc1LV0ibzlSl/VnFcShRgxBX0Qoy1Vomx1ihhYP3h06W0nOuyIE4NLP/d8/L5v79DWZ41xtJA4EzA5iydXo4rQ/QNH7hV7n/5W0rMOLZTCw5t/YHf4q1VlvzdW03FgNslh22T9u5i2dVSKS0dZYb8ip0+JV2sqVASwZp2qVTSO+f7nDoKktaAr36WxDet6ldHqi0p8ee6+9ezLiQT4q3PbpzJWr6w8aBroACAD7CKnaAPqyRWzu9///vGxRfrIgAQwcpInl0EF2CskwBVgCCSyQJNHCrWSwAV1siGhgZD9HTnnXcagEx+XKdwTiyVWEPzYYGGPZm8tIA3gPsXvvAFOf/8803VlgU6l7Zg0bWA0bYLYIgAHiGmwnILMCc90c0332zcomHGZnCAbVhXsZ6yD6CM5RzrKqAcoI7eAKOZQrvpM/UziAC4R7fkAD7yyCPlmmuukYqKCkOaBaBmEADwO5IApO0Axh//+EeBTAuLOJZzyLew+FrLM/0E5AN6aQNA+6677jLglnqsC/dI5xvL+6afdqw8d/N3TROBZ1G1DAGASd2jkb0GsLETjWI5Sqo1EwvHAOBISd3CwougaMGFklj7e7UI6TByn5SWR+XYkzabiU3xmHcw4LUF++apRFSK5gwdOMooNqFXSzs0vgrTrl6FA6Ks2t2lZhrYNvwSua1LuvdogTnDF5rge0KLl0qvcjI4tUiXS/wJOXxSq5lGU0FSB7n8b3M3u3umjt7xzqP1XZWUbT1+2d6XAz2zDOvvunKZGQzOts/N2277px+Z97NSDpm/gffIUK1QIq5mOd493Z0R+eGtv5XPfevKoQVdtqX45HdKbK0OtmrGAARgW1UWMVNOqtCBseJ3vCunom4oFFz0NolvWam5CfmuyUXUqj51ihQFCwNcuWjrUJXJfNcdqvNO2PMA4OyE2yvWPUAkZE+kQbKC1dOWw1KJS/O//Mu/9KfSaVTghmBxpSzWT4Ad6YBIDbRq1SozJ5b2wQcf7Ld+cgzlbRokrKK4C0MKBXDE3XgkoZ1YaqkDAYBayactAH365JwAf08++aRJK/Txj3/cDAbgEg0gpB/W+snAwU033STXX3+93HDDDSZPMG0AbLKvoaHBWF2JG0YAyLgXM9FvzoOeaAPMzYBkBgOwiJPeCDn77LPNPizoWHDt8c45OsCNnUGNefPmmThi6iaemLoAuBBxwTiNwCSNMIiA3rFo0wbWAcy0jWWrW1N4HP7zx7slmMFcrFlr1cLrU7dov0T6Jpajui1h3HkHXhSQalSHhqaiGoeqeFNNTk3SlGkOFslslcH0PJwwZpOIqJW4zN2EOc2P/1WZdLt04GXkQazh9FikcayzSjql6c9PDFfEFdvjkYSEe9IEd/vbYQa8Otvd7dmRqbsXnlwj+7ra9frkCh16jbItqrDt0d8+Oeg9nlmPG9fXvrxZ9u4ciJvslLRL6XB6REfdqksk1hOXB3/0eEGnqouSk8/vS4NkVJP3P1KklSxNG0LyPngCHhBIPK8EWEPv5eG76pHistc1RVLP8EUKew65BgoA+BCoPFsaJAASzMR2+vGPfyxf/OIXBaIlrJOAYyycWBsB0rgBE6OK6++ZZ55pwBspe7DO4mZNPKoVQBtWTkAycbIIrtAAvcz4U3sMc8Av7s6kG4LciZy5WDBffPHF/W6Ls36WsWADHI8//vhBu7AY2z7gzs06/V22bJkAlhHYtLHCYoHGCr5mzRpTF2UAtkyAS0AnbtDoAT0DlDPdx505e6nLHm/nuJVjFcYlnd+CunBzxp0aPeEyTT5j2vzUU0+Z9tnBArbTDgYM0CWDDrh8A4CpY7xL+3PPSr3mWR2NICd7P5W4zK9j+VvWjGp1z378xNka3bBOIi06WDI8xh2xs6mkR7p3lEnS5cQarc+9KPMq2jXeNx0CMaLSsuz0qavv4eoq3bpigBshS7EJv6n52Zc1HUqdPjMHBqvy6TTH7Wkul31PDbj153P8RC17352PGODbJRGTuxbwppQ55k+pcgz4jemejWu2y47NIw9QT1QdDdevp//8ikTCadBLGfTWavSI5tJ6tPOoWn5bZTDAgDX6tedfH65692z3hfS7RdPE7ccjkoHWiB4rPuVaKIik4uoa3vKqVBye5pMZVSU64F86u0v8FToAtqPApD2qvg5hgQIAPgTKzpYGCXBFHKqdAMC4PQOSbrzxRgO4sBgDmM4991xjocRSirUR6zDuwVh+YYNGYJ52CiAR4IwrNPltEWJlAXLZxIJfyKYAv8sUVGZjgd6ftjjPh7sxLsGZ7cBtGPBvc+3iwoyle/ny5YZwijqwnjpZoIerC4Bt62IZS+ycOXOczTCkWmzA+p5NYKpGbOwzyxbgMujgZIE+5ZRTDCC3pF+0C4t2fX29Ae/vf//7DVkXAxeke7J9pE6EWHAGHpja25VdcIxLTAdcSr09MklzfuZndWPEVJlOK9uVMTGqo6HpfI5jvLsHrXm9yqrZvT0kybBPR+fzOw3lu3cWS7ynTHrbBiwk+dUyMUrH9RlZrOQux9VqWgr9KM5XTp68W0Ia+5boyPGDJt8TjJPyseY26dLxuT0tFXl/KPNh3dEdMvGEkV17x0mPD00z1768yZyIK7NHrZNd+j+ssLdb5/rGMy67FGDYYf2qrSwWpE8DO7fslURs8HsCPWIJblf9dem8U3XYZnSatvw6lZdUQrd9e8b+O9XZ5oOxHNcB/UiyRkO1SD+Y+xkoy8BWjx4b17SVBVENdO3WaBslvSpJStWiVinCSyvb4KsCX4/GW5fO7lZGeB3EMRkbthdUOIY04BtDbRnXTQGUAladMcDEhRLrClMzgMjG0hLLCvOyUwBGTJdffnk/GRTuwxxL7C9xs+SxhWwLKy7Al9hWrIuUsYJ7MEANgA2IpD5y2xJvjCs0llWssFZoCxZkwBcu1gjWU8pNmzZtCAt0Pm2x53DOAaZYWa0A9jdt2mT6xTZyA3NeytD+6667zoBV4pLf97739bMlU9bWhZ6JtaZtDzzwgAHNL730Un9dlAXgYw3GugwhFm7pgHxcmJ2Cbh9//HHjLn7VVVfJZZdd1r+b8yEAagYTnCzQK1asMBZh9lOO82SyQGMRxmpv+0hZhHhhyMuQkSz0psAY+JdSMgekriyslu6UWo0UhGVhLXY2lXQpAX0ZNNa0GHIdT1ERnRWlNXYWc9cy/Vfg0LqmSiYdu88sQ+o8mvQm9IOkOSiRXUqAVa6Qb39NyKOdaJztn6yspYDZv+2dqi3nK28kS2bKWIyPV9Bc2Ze+ZzQegHGmjrybi5ujR98bzUp4RTx/XXWnDsiOXk1SPRHCGtu6fU+abKhwPQ7WWSIx1MUjuwsv/GNDyw6uzV1r8Qzw6+w9d3iCB+iIopreH7PniHWOw53oQL3S2lvLpKa2U8kVNWhplHubQxJxn7S3lYq3Qgvng5zHoYpybXIqxT2afrd4AympOqJdoq0BibZoOr0oGRwUD/tTEqiKKfBVjg5dNsIOc2yuZyqUO9gaKADgA6RhgCkuy6TTyRRYfz/2sY/1p0HCQphNsIpCygRoJg2SFWJYiX3FhfnKK680m2+77TZjdcTy67RiAqScLND/+I//KM40SNlYoCGdykyDBDAk5REWYcCxtYTm2pbh0iDZFEF0ArBHrC+A/YMf/KDpVyYAxIILsEUymbapi74Sj4vOrEB0hdi6+LDFBdzJfs1+iLFwUbYgmBjpf/u3fzOkWOy3LN0WBHM+BMt8Jgs04Nqej3IMZmSyQGMBRmw5s6L/ANNWYLUe6xKcogBDX6Y86ScpU2RISXL2dJZLLKku5xlkV8T7IrXKxDuptLs/BYhHr3VP0N0uVUWVtfqm1A8LrBQvTJIydZMK1UbTzM9ZPk56ddyBGMvu7aXS01ScvkzUku6tSAOP9Ab3/feVl0ksknZ9nBSKyhlT3pBX26qlJaofJKov4tOt6BVqLt3aoKb4qG6VcnXHt+JTDxM3S7CuRrwlmhqlKyx7WyulJ+aXqZPa9RJNp0XK1A3Al7u7ua1cy6O79EdhqL4us6ir1w9Twr89O0a3nuH9NWdBmkfC1QpzdH7qLM04oHnM0iR3jh05Lnp9XqmuGxhwz/GwCVfMp96DDHBxj7ZomEJxSVRKlJFc3yjmecjrHDEYzbyydVBLmckjEd7Rep+rkcFXVW3KuP2fp0Sfb7yM+8SjTpVYeI2V127MNvfrO7vU3Xwd2dTyVm4rAOADrH3y5loBFBHHihsyVscFCxaY+FT287IbjgWaFELZ0iBhBbUCORSSmQbJ7rcs0JlpkOx+55x4VWcaJFyxIeSi/cQl4/ZsBSsmMlpbhkuDhKszoBMrKSD+2WefNeRRNjY2M0UQbNC0LZuEQiEDVnH1Js6X2F/cza1ebV38BoDfY4891sQzNzQ0mDhigKplbsZCTF8huYKYCxItYrFhqcZKjtXXsljjeu5kgQbEA/jt+SiHNR39ZbJA0w9bLlufxsO2EiUE48VppUwtaGW1LRJV96ouBR2xZJFhfg6oWynxviU6kVLFKYHaKn3x9r11nTtctOyrm6yjIX0vUgVqXVvKJbyzREFwjwQVCBdpzl+INlKaFzgR8ZoR5p5mjcVyxmgmY1JU7W7AUXHkQtm3d8ALpkKvx6WT92qOX017pPl+O+MQs6VzVlfotVgfikipDtpkSuXi9MBZ5na3rFctPkI/kgcsah2aMqpDU6VUKltsBQNdQc3drWA4qddfTFNKtXem98P8bKUoqHnelx5rVwtz1cDF15wnq55dr+RgA+kHsymGdD3zFjVk2+XabW8/Y5Hc880HpKOte790kIgl5ajjDtuvYyfSQYBX/6Q6iRovNo/GVYd0CmqGAXLLa3oeX/q+Jwd9XJ+b8RjQYOD97FcPRp/Dc28i6SbfvnhCyjVTO3dQCsOc6lDQXDRzaU5FC4UOjQYKAPgA69lpjbVVA4iwomI5vPTSS81mPv4hV3IKLs+47mLVzEyDhPsz8bBz5841AArgi3szqZUApc40SNQNwMb1GTdcXImxYpLbN5ubH2UXLlxomgL4w/0YiyrkXWeddVZ/E20apFzaggt1tjRI6AKSKFitsZICQAGKnI8YX5siiDhZADJAGjIurNSZYuNlsXITE40wcADxF27F1tUa6zaCazes3LhdQ3zllHXr1pl2EXdMzPTFF19s4oYB1OgaAGzTINEHrM4I1mNyBzPw4STWAlBfffXV/QMe1157rfmd8BKwfXSefzwtp9p2m1fjYEgrEtQ4zKBv5I+8dD81ZVJP63jq8kFpa3LTs8baC5mVlV5NexRW12am0UX1GEpJYssr4p+TPZZ99DrGf4nJ55whrc+/JMnOwc+IMv2wK/PnFteL9XfyWaePf2W8iR6UzJyqH8I6JjOoDnWb7Cox06DNw6yk9PlWVwDAg7Rz9ntPlJ9/71F5bvmrEo8OHXixhf/z1//PLhbmfRp42wlzlT03tF8A2Ku56k9917EK8lwcZuO4kioXzJCmbZuNF1F6s0eBrt9MjmJDFglzqjxi1pDtbt7gPfmzEv/tR8WTyOV7R80Faib2LrpUvd7c7WU01q6ZgaHbsdayCdSebCzQw3XPusc2NjaaIoBbAO2SJUsMMCYO9oorrjB5Z7FWZkuDhCu1kwU61zRIlghr69atArETc+sGTGPyaQvWWGcKJJZp79KlSw0JFO2mbqzigHJAJgRf1ioIICauFrCKtTWbAFYRBgysAKYBxtTDMmKt1ugNt2S73R7DvK4ubUXDzZpBANIeoQ9czyHtQix5lW2j2aj/7G9m1+1+O2c7fbSTc7s9ZjzNu5b/QdMgpd2n9qfdvFB9HtXtji37c/iEOSa64kHxh2A4zRxKyLGLipt9ftXjS3/K8YCJWWzyeWeKX92g34z4qyul7uxlb6aKCXFseahHn537eT3qdVxaqs+5cG6DDhNCYTl0guf9Dx75ssyZPyNr6eKSoPz0yf+QxnkF9+dMBfEt88l//aCUV5Vk7hp1vUT1+vGbLh61nFsKhOKb1J2cb6J87u+UOSYU3egWNeXUT++8d0kyVpkTeSVu5R6N/fW+4/M51V0odOg0UADAh0DX2VigAWF33323mQCCsBtjHcYVGItnZhokrMeZLNAPPfRQ1jRIdCmTBXq0NEgW/FoWaFygh0uDlG9bnCrGYoq1FtAKGRTnxX0YazC5iq0AkEkRhbV5OMGaS2wylmKbbuiWW24xQJXtWHlhZsbNGis37tTnnXeesWpj4YWwDKswAvEWrt5YgrFEQyyGWzXLF1xwgSljWaCxLjvTIMHgTf1W6BMg+vbbb+9v19e+9jUDlAHLFkjb8rQPt2om9DDWJb5npwQ0D7A375dpumelGqfpUbanxD53s8X2tjeJN9CrEza3fD5K0nr0F8fUEp+U5N4t6Q0u/V+k9/mMf/yY5qEeuAfzVcXs6z6h1vj9Pz7f843F8r367An5YkpWh5Uy3+sxXb5Cwy1ju3eNxe69pW3i/XDMkvlSJgH9UwZZjUtnCun/cgnKrDnKq1CQrBp470fOlGUXHC/B4twtuV5fkdz2q3+SxvmFQQWr1FTLDpk0xTJi53J/U8Yjk6e3Sqp5u62mMO/TQMd6zeCgoUkjZXCA8wpirNYNDZJscvf3zli8cNz9xj+Av4i1AgKq7PTtb3/bkDwBcJ0s0IBfrIG4LzPddddd8qtf/crk+gUgchwjnwBNADHESFhIzznnHEMEBYM0LNAQXjU0NJhe2DRItMNaGnF9ph5coTmnTYPEfiu2LZks0AA4iLg4nhjXRx99NO+22HNkzkkxhD7I7wvQxC0YsGmtsJQnPjeoJEmAY/qJ0NZMwa3b1kW6oaefftqAUfqDoEN0wrHE5QJO6T+s0RCWPfLII6Yc222uYEAz7cEdnTaSzog6LAv0RRdd1J8GiYEC3L0ZnMC1G6EcVn9nu7AsA9gRgL9T+P1pL5ONhXbuH2vLvV3KIqnWxzSQ5VoauJ6Gb2u6XKnGX3pNAvmUuqzal/HwR03kPalo2n3KXxI36RJy0yMaUQbjkpimZUzfD6nOlomsppz6Fph7hGzqqsiprLNQUi/L1R216kI+17nZlcvJzg7xqLtotbK7p8nrcrmvUZVaidRqXF/Voc9edcnXd1ZBhmpg86ptCn29UqwQuFRBL5NGYap1yCNvvK6pVQoyrAa++qPr5fR3v11BcJqIctiC+l4qryqVm777cTnprMXDFnPjjlRPWN3BkzJ1drOx6no8Q7+nrF7YR1xw/QzN2gAPRd+7yu4vzPWppwSU7WsqlZSyRK3B6nGo2RngxjJTPL0e2RvSMhUKkpV+seAZM+Yum8GBkGOueeOnQQBFJBcWaKe10NlD6li5cqWJ2wV82jRIsDEPxwINgHSmQaIOwBqAmjjVTBZo3KEBf1ZoC+BwOBZo6oLBGisoU65tGY4FGmDN+SCwsucmjRH9cwqkVRCHwTpt5YknnjCu2SeeeKLd1F8XbmYAW2KFcRPHeo28+93vNmRWpEdypn8iPhhXaRuPC6kXgDdTsCgj3/3udw0oZxmdYBnGWsv5sAhzbrYjlgXa2UdAvG2TLWcK6z8s2OgAsW7dZmWM/vNWaT7BJo0D5mOjpMewxcaUOCMtunGQKMukrpN2oSQY6wO/ukF15at1N3lTUWml9MYiRo+hipjEIz5JRFWPBndk6hElwtgJ+FVSMSXIslJUO8Muunb+xq9/L1uViTwS98qR1frRpnoqyqJCq6Be1XFSgcfq1mrZ01Miux74g9Td+BG725Vzf22t9OpAIPf15MpOaQsXS1RjBNMwOLsy4YH2KdldGjSr2tQcElCynYIM1sDO9bukae3Ae9e5tycSk38//d/kezvulKp6NaEXJKsGvvGzz8jv7/uL3PHFn0hHa5dEe+IS0wkpKQ/pe8YjC4+ZIzfeerksOr4woJWpxKKyKultSQ9AT5nZqjm/Q9LdUazfUIPvbQaxSisiUlbRYwa0qIdjCzJYAx4lTODZGG0K6RQUb3FSinRQmnd0b1w93Lp5l/fptlPzBRd0OFiBY2DNfrWOgaZMjCYUWKBbTA7c4VigiQ0mhRAxvrgOw7KMa/LNN99sLKaWGAzmZwDvpz71KSGtETHNsDJ/6UtfMqmipk9PuzZRF3mMGxsbDSM0TM+//vWvjZuzrevJJ5806ZyoC1IvwCguzADgVatWmQvPElxxHguKAeqkkALc0m6bjikfFmj6SAyxtfJzskwWaGKjrQw3OGL3j4W5t3og7Q4fy8XKDhtUdt24MsPGlQk6nQoJ5ylRwJsUv5Jj+dRdmrJWensiEpjZaFddOS+qmSq9rQOWH3+xppoIJiShRFiQYaU/TPSzDuCr+jPu0up67tQjSvbOTBPYuVKJ2uloc5vsVADs8wRkV6TUsD7PrWiXSRrPygeKBcMW9HIZNvWE5PWOSulKBCRUlJCtP31AFvzD+yVQWe5WNYrH5xdfaUhJcdS1XpVUrczPcQ1X6NJUKFG9t83HnF6L9qPO70uoF0hMie8S/dck93VoTnrw0bWKzOj4D6+7R/7433+SUn2PpH2EBheo00HoIlX4jUd9Vr72wldk0qxJgwsU1vo18HcfPFXOu/gdsn7lVnnhr6ulaWeLWoWD6qZbI+8472ipn17bX7awMFgDvsOOlljLLrMRkFtRpezuOiUUrCU1cwNCWJPPMbhqNuo/ji3IgAa6/+/nkopYrgPeKBqMpAPYyREi2CJP/078M68fqKSw9JZroOACfYB/Alig7QQYw+31C1/4gnG9xR3XChZLABoT5bH24k5rCZdggUaczMvsgygKRmLiWz/60Y8ay6EleXLWDZDC9RnwZl2h82GBxkoJ6BuOBXq0tuAW/K//+q+DJtiTAaO4KeNaTP1YhAGcuAtjtUaIkcXye9JJJxkrtmVzPvvss80+3MERWxeWWNyWGXwg1tZZF9v/+Mc/CqmSsIjX1NSYZSzjiGWXxn358MMPN4zOWGGZcEfmdwIIY+W1INmyQJOj2LJAo+fhWKCxFsMCTXl+NwuwTQPG4b9kS5O2Om0Xss3Xy02CmlKhTPMLVpT2mKlc5yWhuALgweDXHpNoph73ireUmLbBetTQaCXGUjZttQgXV0V16pFQZVSCZZqCRt3XACaZ4i0NZG5y1fq2B/6kLmgxqQtGNKqy14Dal1rq5PFd0+XV1hpZ314pmzvLZF17la7Xmu0v637AL+Una07g3lhctj/8uKv0ltnZlKbkCvladfOAd4FfP4irNQ3SlKpOYxWuLe+WOrUO4+5cWx42OcCd16SP9FIdA4M6medw2/qGZ1+Xx+5KX1eA3AbNS1uuc+78kN7LkzXHbZlOSKQzIv/zibvMcuHf8Brg22bBkkZ519+fKnOPnymHHzNd3vfRswrgd3iVmT2RxtOkp99Ta6AwgDcYSpgpG/jlmJ45pw8c4PKlVCIuXT+7NT3Cn7MuUhJ++H8kvn19zkcUCh58DaSfvAf/PK4+Q4EF+nQD8LF04oYMEDz++OMHXRMA5meeecZsA3ASg2tjcm1BJ8BkG+UBqLwQsehiKWbdWReu1NTH5BQYrhHALIJFmfRHlEeI2SU+2+YJxnXckldxDqdkujTb/XZOWQCynZzbnfWMh+XeaI/Ed71ZQgwsmhor/fRj46HLB62NnvZ1eb5EM5uibr5qXZftKzJ3uGp9x8NPqOtuTKaoxRdrr5VEqkh2q0V4c1elrG2vkS0aI7w7UiJsT0vaTbpej0tGemTHg/9nD3XlPL7xJSmfpIMsVj0ZWsBqBCAmp3fGI9CUJMyhsjYs0ZfdfV871fbIHY9oWEM6vIXtPPsnKQie4ffJVB0MLWXksE9S6qKw4ZkN0rTF3QODVh8jzbdt3inHzrpI/umaW+WaD35ZZgVP05AkMhMUZDgNrFwXlHb15sj4FBquuNmO10x7JCSvrC04i1pFxda9YOJ/sz0DbZmBOe8jfV7qbU4cdc9TDw/sKiy95RoYePq+5U2ZuA0osEAP/LY7duwwVm4bM233AEQhjwJkApCxcs+ZM8fuNnMYqhGbaxn2ZEAl1l3ioGGNZiLGmbpwcZ4xY4YsW7bMsD0Tn71nzx4TZw1gRj74wQ+aOazQ1AUR1erVq401HhIugDKM2oDvg8UCjRUaVmymTKBuGjeG/iX27lQCDW/fB/AA2Mi9iX3HJJQNe5MCQJdKKq6xltEO8anbM7G9+yv+koT07lqzv4dPiOO6d6Td+vwKzBaq63NaRtOpfpRowSVVLeLT45Du7el6zIoL/yV3bpSilHpwVHfp/T14wDAXdYSUlTwUCEti88pciruizGvL87s3exVxrP5Lfse4QpGOTsbUW2PpvEukRwFve2unxgLDhq8u5Ffc4ihVWMzUwLonVsuvXjhSEsqWr586owpl4FH46YpFsj7P63jUysdxgdj6F/vdn81goXl/o9Bsk77eLcpSD5voyr+O455PvKYXhnUO8G9q2ZipFtdcLIkAYNxyzz///P6zAbawMGYKLrWwJAMQcfW1LND/9V//ZVIC4UKLwAJNnOzll19u1jkvOXatMNKM6zP7cYX+zne+Y1igL7nkkiEgywI/WJIRUgNhRf3yl79srLC4MuNuvb9tsW1iDjC1Ls3O7cT3IjAkk5Iom+A6jUs2OZER3JIR4o2zCYCVc6EHrM8QWcHQbS2w5Dq2hFqNjY0mNpmYYki/ECzL6OvKK68067QdsSzQ9957ryHDoh6s0Zks0IBmXL0R0jLhDg+JWGYfyU38+OOPm3K0berUqWZ5LP5LtGgUm+qF0c9cXqKD+8ALoi+mVY9PNO0ZvNtFa6l2dRP1h8RfHJGUskcmNeYX3eQuao0rj6sFWD0LOt1tMcJ6a6VULeLHVjfLq+ruHFdyl151cs4Ur4I7n04LyjsE0Gwl3pm+v+262+bJNn2exnqkvFIJ/TSWv6u9NCcVWMbY2vo0+3Oy2d0DCU6lRbsGrk3n9uGWY+GYdDSl9ThcGTdvb1q9Re6/7j8loC+gmOMFlFSz5mMP/E1+/8nb5cyvXi3B8gFeDTfry9n3zqZ2Y839zuMnyCdOX2HeNoQnZZNYokjCSoB374rFSizoF44tSFoDvfveICVJvzrMt1D/2uAF9jmlt31wBhDnvsLyoddAAQAfIJ0DoAA2kCZZAcSS2ofcs7jzWrIjrJJYQj/wgQ/YogaUAtiIfQXsZmOBJg0SKZBwuSVmmHRJmSzQDQ0NxlKJJREglckCDbB1siHTFurMZIE+7rjjhIm4WkDZwoULB7FAj9aW4VigYUi2jMeQSpH7GN09/HDaNSTTnRjQ/dRTTxnWaCzCgEUrixcvNi7LgHsstcQTE1cMSRVAlthbBCCKPgHxEGQRawwj8xe/+EVblZljSb7nnnsMYH7xxRdNqiNihq3QdoTYZGJ6Ab3kNMZ6TZ5l23bbR35HrNH8rtQ9XB+5NmyuYQYbxrJ4y5SltO/DQ3GwXrcDAGJkAJcuZwid+l4K3srqsdzVg9o2T6n2PZFmPg+UKwt6l7ISR3MBwapH1Z+/lJjgvpdwsbuZY4v0vk863B8D6oq7WC27EF3tiwUlkvQZYjauvRLNcVsXjMokjfv1ZnyceEPBg/qbj/XKi8r0mvTrMy4elarabuUrSKqFrUyJnVFUhrJMZ3CFTklxWVSqJ1lCGB0fqxh4Zo71Ph/s9vlDAYkqqM1VfEF1i9Y0PgUZqoE9qzbJL973zzow0633s/O9ky7r023rfvs3WfOr5fKPa34kxdXuJbQbqj2NOVembARA++3HTpTjZut3yeyd6ZCGvgPQalLv9xe3TpPntk6XGOR3KsVlxWZe+KfPt6q6IWrIBLpDCvRtKCrNP1XfcHUVtr95DRQA8JvXoakBS2+mAIhgKMayCsC0JEqAX9xeyQ/sFIAaoA6LJpZHgCFiSa4yY2LZh5UYoicL1GA4xpXWpkECmMGmDNsyFmJItWycK8fTFgAu1mRALvHKpGKCMAswiTswbsFYMJFc2zIcCzRgEYIvgCxAHB3h8myt15YhGev5HXfc0X8+rLEcay3gtIV1rNUMJNjcuhBMnXDCCezuZ1vOpS7KUxdEZOgPwXoOcLZt43wIgxQAbNypEQYKAOq27bn20Rzcd7xd/vrXv24Xx+TcW6M66E2ne6KBgGC+RdLfI7w+s4t9Qdg5JmT/tFnZC7tha1A/zrwDj99AWUKSSkYSD2tqBVInGFU6QYdu0NUiHbHH7RnLr5Wimpl20ZXzYE2lxDt0BMEhgNspxT1mYjPjNCOlRaJMqM69AzL031tdrwTPPr3MoqxKWaWS2Cm47Wwvlkh3SAcuCX2AywDehV4pVsK7ssqIyS1qDtB/OJZ76xvtquvns982S15T19NcxRfwSePRDbkWd005wO+9Z99o+ov192z9VnqkzyOLpyR+Hh9QL7JeDa3h3fLT8/9JPvynbxYswY4rJBDBs4D3ht7hCmyf3DjbTJPLu6QkkE4n1R0LSFNn5gCMehv1FLwSrCp9M+eJp0QHBsOD3zl2/0hz/5xFI+0u7DvEGhj4AjvEJ56op8PF1gq5bbEAfv/73zekTrg8AyoRAB2WSyQejwsWU4DqY489ZrYBEgHAlgWaOFQspoBGQGBDQ4NhhL7zzjsNSPz7v/97c5z9xzmxVELihAvwZZddZkAt7s6ZQlsAvwhWU3LiAlAB7jBYW9ftfNqCpTnTlRdgyITrMqD3Rz/60aAUQVjILUMyurFgGws6rsroD/dwXLMRQD/AF+sqgJTjAfqwPudbF1ZpwCcu0wBgBgBwS6b/1Dl37tz+AQzq/+QnP2mstgxskJqJ4y1hVq59NJ0YZ/981ZpmokQtjt0DD3/93uCbow8EZ+8Q+52Ca2rZSac7N7lq2aP3nKfxZEmtHnDf92oOwaJATFJJXKKVn1jnpJzxQDrkTZk0SEU6d0qi1y/BxRc6N7luedq5p8i67/1MP+uGl9HAL1qddt6pw1fggj3+o95h8gD3kRKbHnO9VdaEzcSGpLrrsy3zfrbqSarrpGfuQCiO3e7W+VkfO1M2v7hZwh2RnFRQOblSGo9pzKmsmwr9+bMD31X0e0EoJFXqYbdKOUPwmzlevyfKdN2IfuN07WmVF777oCz97CXpbS7/37Vtl4Ra1NrrUW8Zm5u2Tyd7lSF/JIFYMLRPjTYVVWURAABAAElEQVTKkVA2c+yGZ43UhwO5L3jkUh1GGCC2y7nuEk25d8J5ORcvFDz4GmDgrCAHUAM2tRFzgFm+aZCwAiOAXwQQiIX2wx/+sAGkp556qknRQ/5aUvUQW4zLr5M8ifKAWmJ/2Q6D8Sc+8QljzbVWS1N5ln89PT3yk5/8xJyT3Zb4ieV82gLQx03bOQFKAdi06QwluaIM64BJ2ssybQfoP/DAA5xSrrjiCuOqfOaZZ5plrK+4RCMcA6A/7bTTTAojYoeJuWVbfX19XnU9+uijZrCCVEi4Up988slGZ9SFmzkCKRZ9YE5KJUA8MdukpkJsbuJc+mgOGKf/4sFa/Q2HNp6P4uGmzNIwnnpq3f0y7Sw7UWIZaSnQH9Zdf4mmQlLX6GBFXLAO+4uTBnhk6rE3kZLe2adkbnbV+uQzTh7yUZevAvgonHLmO/I9bEKVjyhoaOvk+Tx8t7x6bXKNDieJXq+07czd5Xe4eibK9uMvPE5mHDlDB7FGUJqjs1d++wrHWmERDex++XVp3Tw0rnyKvqfPLiuTM3Qq9/aB3z6VJTQk4qUf/j5tES6oUXY+9oxUFGk6vSK1kKfdi3LUioJfPabcG9U6VuR4zMQu5ikrFe/J00UC+cEnT12xFB3h7m+esXZl5PcLjrXWj5P2ZEuDBAi0rMXMf/zjHxugB9EShFAAaCzDNoft9u3bZdeuXQaYAQZhS/7DH/5grLO4WdsUQqiE2ONrrrlGAMm//OUvjZZwhcZV18apZlMd4Bd351dffdXk7yVOFist8bD725bM80AUBcgFTHMeADmWV8AsbsQILtgIgwG4FVs9YTnmWEt6RR/nzZsn9913X39dt956qzkWUIzkWhcDFQwk4E4OMEeHWO/5neygBO3DfRw9wSJNX9C7BeR2sCCXPprGjdN/HTu7jBvk/jafD+xwskK61qzb3yomxHE7t5aoIT2/tBTOjifU2rZ150xpeV2JyVws7V1J6ZJi4+a8P2rAPbpDSqS1zd3ArfXl1bJ73xR95u3fZ0Gvxg7u2F4j+1a8sj8/w4Q8xqspj77wyP8zg7LZ4lZtpxlzuOaH18hRpx9pNxXmfRrY+pdXNDQkPzIxq7zdL2+wi66e73ziOc11HpO5ZZ3qezXYi2hkxXjMMb3RmOxa/tzIRV2yN5HYKP6LNOXmlFIdrc6904FPHS2JpLu/eXLX1qEpWXCBPgR6zpYGCcsiJElWsCZCngQQvPHGGwexQJ977rmGQAn3YKyNuEBDvgRg+9rXvmaImzJZoHGZXr58uXG/xhUaUPe5z33OxBYD6jLFgl9SDWE5xoUZ4E7csZMFen/a4jwXMcdYrbHQOhmSAfUAeoA9cccIgwFOHdl6cDu2Qv9vvvnm/rqwzhJrDeBH8qmrurrakGkx+MBvhiUckjDahliAi0v4/fffL04WaAYqAM9ILn2knVZw637uufTLBZdudDOWJdEVlrZYiVRXdo8aV5nZD73sJa6xhJGoT+It6VjrzDJuWe96o0k2rZirXhKvqldBfi5V3MIAjnWvz5aq5ja3qCxrP7uUNXdLrFoW+HrUwtE7ooUyswKuR4xz2+M10rWvPXO3q9Zjza0S6UjJ5i2T5bA5u/VdlHv3uRb3Nmn4SFu5eHcpm3RB+jUQLNFn/RHTpHXldqnCa0n3OO3Bcb0Iu8uCUjajpv+YwsKABjq27ZHeOJbL/CSV6JVwcyF2Fa31NDUb5YU0j/fbKtvkZc2LbuOBzY6Mfx69Sn3q+nxERZsE9RgksjddR0ZR162mUnpNqSdM4PNvl+hn/qIfNHptxoZ+UxvF+PQhWu6XwGePE0+lX9/ZTa7T11jucAEAH6BfB5dlLITOGGDiQpuamowLrTMNkmVedp4aoiYmiJfOUPdgBPdh3G9xCSbG9T3veY9Jq4QV16ZBwoJqXXQ5pqEhzQINwM5Mg4QrdDYWaCzIWH4z0yDhUpzJAp1PW2hPpgBqid3NZEjGIgwABgDi7g1bM4CfWGALFokBxqLtjC0m3pa6sAbTfgA07M5sR4h9JvVQtrp+8YtfDEq5xO9FrmAGCiwbM3Xh0ozYNEj8PpRxskCvWLFiUBqk0froTPVEf3C9RjZu3GjmY/kfVviEMuu2dpRIbWU456YC2gC/bRpz5AkS25rHF3bOZxk/BYt0ICscDmnc/yL15HhJX44KxnJQSTxepNdiSGPUjxJfmVddpge7/40fDRyYltL/pMcrK9ur5e01zTkRXnFmLL8xtXa+osf5y4uUk8zdevQUaf91NKCtvVw2bCyS+XN3quUyHdYw0i+FJ8K+5grZviPNjurJcEcd6Vi37IPcap+SBzbrFNLnJ1RjvQoyoqpghr4qioJ6/eVw87tFYY5+FgX8jrX8FjEoFETvYe7tPgHQHl3VLDvCJdKsLPl6OeqzMD0kU6SgF6nxR2VGSVi9fNPrbCsq3NeoQSWtS4/er8HbTpPkX3ZI4veb9WWiQNiqi1FVvbe9S6eL7+8axFOm7PpGCpCrTxFjYlb4NQ7QzwALNG7CAKhMwY2X3LLEjyJYCLMJbrek4QE0wz5shbhXCLVwYbY5aW+77TZjCcbye/TRR9uisiWDBTozDVI2FmgYqTPTIBHbCnDEIuxkgc61LSOlQQLoZ7JAWysr4B53b9rEZC25/R3UhU2bNvWv4haNC/WaNWvMNqzjN910kwHR6BzwTPwzVtZ3v/vd/cfZBfRqBeZsS0xmt0GGZYG3TYP0t7/9bQgLNFZ1615OudH6aOtnbnM5s+x0ZWd9LIqvskKS6gqeSPilua1MKsvCCtyUwGmYbw0+opkiPQHpiqRTMRRpuhV/TfVY7N4ha1PpjMkGXUQiQXn00SXKXr5B3e171BMku7UDgJxMFmkoRI2GJcxRnRbpR0mRhGqrDlmbx+KJyiZViNfvUzBRJCtaamV+mTKy+xJK+JI9XpVrkZjfTr1+13eV67Lq0O8V6nGzBJRN21usbM+aD7mjo1ReWzNTZs1oUq6D2JD7m8GDXr0WEzrteGOSEgemBwnRX/H0KW5WY9a+F0fjxuqrapMIF2D/l3K6eGdrt9RWq0tlQYZooHrONPFpOqlEz8C7ekihLBs8SgVfPj09EJ5lt6s2FU/Ve/S1Ac85gO2csm6ZnQpLh/JQxPUZyCVJCrkyf8JYfzMVVDyloEt0UlRUo4MGZGtgYEEHs5bNFO+pMyS1q1tSbVFNb6heSBUB8TRUmJC9AT369Nix7d030FZ3LBUA8AH+nbGYWgEUEbcLIzOxqQsWLMiJBXq4NEiwM1u54YYbzGJmGiS737JAZ6ZBsvudcyx6zjRIMFCTDoj2YwHF7dkKllpktLaMlAYJSy11Z7JAUy+DBTAxY70G6BJ/jOUVhmXcxHHrtmRTlMcyzoABMdS4MN91110mBRQW5I9q3l8AsR0gsJZj6sKVHBdm8jRbYXAAK/kVV1xhN5n4YkA21mFrVcb13MkCDeAG8DvTII3Wx/4TjMOFiiWLpWnXbtPyRNIrze1lEtI0CqFgXPwKPKwwspxUC1uPujtHosS6DiDkXo2jrjh2YODGHuOm+dRTjpHV//1zScZTxhKMRbe+vk1DD5o0TKBdX5YpBbz6gtXUR9GoX58l1eohMEUHhgZyMiZ7NKftkvluUtuQvs4+fq6S3aSvO8Ds6s5KA4DrgxGpUVZtrBpYOOy8Ra0ee6Ih6TI5LtOWj6SmT5l57OFD6nbThtoTj5ZUfOD+xTth7fqZUqLppCrV06OkpEcJApP6LNR7Wgez2vS+7+piQCutQ3RVFPTL5FPf7ia1jdrXn3/2R5Lc8EY/AM52wAx/kfznO78in//rLVIzQ5n2C9KvgYZlS+Spr9+XNwD2BQNSO39mfz1uXqg/5XjZ8ednhsT/MkhY3ZcCaST9KKSTKYX72qjI652l84FnHhsBwp7pyqbNNIx4PAEd3D5qmL2FzW+FBgoA+ABr3YItZ7UAJ6yoWA4vvfRSswvQSaypU3B5fumll0wansw0SLg/A/5IxwPQAvhiOcQ6CSh1pkGiblx/iOXFPTifNEgwIANAAZXEAJ911ln9TbRpkHJpy3BpkIg1Zrr66qv7BwOuvfZa0wcs6Lgq034Yrp2C6/WHPvQhsw89IT/72c/k6aefNoMLxDgj1AWjM9ZjYnbpg40Zvv322/tdntmPu7rNb4wFGWv3kiVLBp0bl2zSIFEX4Bjh94QFGsFSTrsY+LBkWbn00Rw8Tv/5qzUN0iDxSI/mD2RiGBngS75Q4gIzXxTpw9IWEL9eZ26W6sOmkVdGVWAHBjxKCldtJvQI8A3ox0kkEjDW3qG6SumzQI9W66ebxaega8HZR8srv37KjMpzzXWpdZdpYzfaVVZttWzEdQCGD7lM4Xo96p3Hu94FOlRXq5Y2dSfP4BsKq9cGUy6S0mc4QLogaQ00bd4rz/z0r+LXi2xJiU9eDKtngu5SZw5zJXI11iv4nRrwSritWx665Vdy+Xc/lj648N9ooObw6TLt+Pmy+bGX9LHIu2N0wW367Z+6qOC226eqbgmlVTf08Te6MrUEau/SOgrCt41XPQvPlZ7Ib3VlYMBwRN2o/ry+RuX2UfbogowZDdgvrzHToInYkGws0MP107rRNjY2miKAWwAhwIz8tO973/uMhRKLJC7V2dIgZbJA55oGyRJhbd26VU455RRDCPXDH/6wv6n5tAVA6kyBxDLtpS+InbNMvLKd2M4yrtsAVit33HGHie/Fort06VKz+ZFHHjFzpxWX4+k/QpwtdQFOAfaso1/cnok3ppyt6+GHHzaM1NbKiy4QcjPbY4kvRpxtZ93+Ziwjdr+ds832j7lzO/vGm4Rfx5WKDxGmTOH30/g2Y+0d/m3rKw5KWC3ubpbOl16UumpNb+ThczhTNM5a46WxwuHqnE2waNYE2iXRx56erYxbtp31mfcOsW7YvgN6o5qeJxv4pQxWkDM/faEt7tp5Sr1ySrxh1Va2+zoXtaSktMwjsZ1v5FLYFWVe/O0KiUXSrrs+fTcdpyD48KBXZmkKldkKeo8q9skMnSNJJdN55eEXdEwsewiEKxQ2TCfPvvXjGuIwzM6MzUl9xybUALD4ivMy9rh3dd1jq6RZvbCSOerQqSnCHTh27f+tdG529XIodLZiXwW/eq3lJPopVFL84ZyKFgodOg2423RwiPScjQUaF+C7777btABQBIEWRErMST+UmQYJ63EmC/RDDz1k0iARN0zsqAVzVJrJAm1doZ9//vmsAMyCX8sCfcIJJ8hVV11l3JTJZ7xo0aL+lEz5tsV0su8f5wFQAm5J9wR4xW0ZEMkEyCRuF8sz1mvOy3YAK+AX12NIxBDrcvzNb37TtJV1XJ/b29vNfnQM2MQyixvz9ddfb6zBWJMRyLZsXfQR12Zct0lrREw3sbyAZZiwaTMWcIS6SIPEYATbcL92km3k2kdTmf7jvDauGcv0mGeB3tfc705q+5Dv3KOxqzG1rpf0kX/le/xEKB9vbpEKf5d0+jQdUhzr+fADBtn6W1faJcFQUKjHp/eSm6WiIiiH1cRkQ7Nay/PU4/zaqJRXFawbSQ01Cfh7JRhIqDcHxEP5XI9pz4/SspRejwW2WHsvbnx6vSRj+qHcJ0X6Pqr06WQ3ZJk3b9UQiMMKcdRO1XjKS+TBcJF8QPOjA3C9qsds0qNorVVzo6+bXCOf7RsIz1bObdtat+/TsI9iKfXFpdjLoGtuGgD8wpfAsYEd+3I7yAWliAH27dwo8ZkNCoJVmcMSoOjgtn6/+pVo1nO4fpf6lPejIGNGAwUAfIB+CmsFzIUFGmAG6MV9OVNwqYWwCeskbs4AYnLaAgazsUBbAiWbBol2WGtjJgu0TYNk28q5bVuysUBDxAW7MjGuEErl25bMvrFOPC8WcUCpTYPU0NAg5OGFdAuXYyy1uEBv27bNAGXiawGyl1xyiQGdtl76STwvx+D6jBAnvGzZMmO5tXVZwEveYQA2lmjcrdEZIJa0SZBWIQBQLO0IZRBy/yKWBfqiiy7qT4ME6D711FPNAAT5f225zD4uXLhwSB9NYf3HgAITYtthVsbov17VId8fuJam2SMZBc3ljZoeLfX2MUv2RjJ8Lcdofw9Ws3ojel3p/TqtokO2tlUriSSWoNH1iMW4prhbKkP6OxSFpLcnfe0erHaOh3oT4YhMrVTLT0+nbOrObTDAq3qcXdIltWV+JXVz97XIb0xcPgzO5aGw8eCIqQdCLtcjDr3c09WlEX0ulJt6xsM1cyjaGO1Oew3lei5I7aLdhWsxU1894agk9Nq8Z2+PHFtWJHNDhDOk+Xh5q7CsnwPySrhXVuk0syb97s6sx63r8QjXocc8GxeUkzYvNSoITqpSI70+2dwNwZ16dpg63KrBof32pBLi37JKkpWTpbfKAlt9f/MK52LUhaLuNvG27tLnYpmkkoX39FAtvrVbCgD4AOnfut3mwgLttBY6T08dK1euNGAX8InbMCmOAIbDsUCz35kGiToAuDAaE6eayQKNO/TOnTv7T0tbAMHDsUBTF+D3ggsuMFOubRmJBRr3bQA9QBNwTbwxVm/EgnOsvVhyqQcB7OKajXU1FEpba7DQYi0m5RCpnCyYt5ZhWxeM08QFP6HuzAiAFpCNHu677z5jVcYCTFuwonNe2J8BtADqn//85ybO17JAUy+pkQCrAGYswrTPns+yQNNOq1/YuTP7aBqj/2CxtsJgx1gXvw4YRJXcLQ2Ce3MAwbwMeB3oS7fPm5d3RKDO3ayS/tpJQioF8gjOrmqVfZqWor2nWK8lNDUUCAN8cXuerJbfsmDarTKlHgr+vpRfRsku/efVgahoR7dMCfVKqKhNNofLTMxvgtH5QbrEeqQ5HDUmeI7qsdIfl1hnXLx9qc5cqj7Tbb8+R4nhRSqV8CqsxGvhKBZ1ZOj1yEe0uY99+jwsTg+KkdqMegqS1kDdnHpZt/y1nNWRUBKyqqkF/WUqrFoZ2nnH9ujF+GRnr6zQqT7gkRJ9fOLW26le481q+QUIIzMa69MLhf9GAxVTqmXv2h267JG1nVVSpwSBkwLcszwPBysJffIO2qdkgU1q+bX3fnl99eCCLl/z+Ko09VFEfO36/ccUKpWUD8+ZIvEklfW9p1s8qb4rUq3unsBUl2ts7HW/AIAP8G9SYIFuMYB0JBZoLLFYUwHBuFzD6ox1GbHgFcD62GOPGUAK0dWNN94od955p9xyyy1moizxupZtOZMF2lkXQBfwe8UVVwjW2/Xr1xsrO+CUZQRAzES7sMwzEEG7zjnnHEOWhYsy8dZIPizQI7XLVDYO/5UtOEy6Xl2lLVfmQ315YgnmI9kMeg7TH4AbZa306suheM4cu+rKefFM/UgzJFhpPdaVhqW6OCKdGm/VqSzFCUib9EPEADZ1XStXRuPyYBpo9CssoWmTHEzm/dtdtrDyoeckqhb0YgW2VUoctsTfKu1KgtWiAC6sOatJ8+HXAYRSb0KqdfCgSoGvlR49buVDz8rbLzvTbnLlHOtvUMNLIn2hHiXK6g67e0TdoWOaKgVGd+5zbmM+nIOaLiWkevR52ZoWvENK5s2zq66fz/7/2TsPwEqqqo+fV9PrJttLsp1dkJUFFqRIkV4UAekgxYJgQz8/P4piF7GgfirKJ4KKCIiKIk3KovSFpSxsZXtNsun91e/87stNJi8vyUtYlyTzzu5k5s3ce+fe8+bNzP+ec/7nkDmy9PYnNK2M4+Y3kFaUvKmgPD0PhoGaGWvHmNhftGS+/Pux5WZoOJVvD/Vcd87x4mb+gdOWOHe5fnvmEfvKxmfUWmm88T0KbDUHsILbfH2u5Pt1ElXvm1yh5EVviWi6Qr13OjkTfIoUZh7Zm7TV7Ur1FR8j4ap79F4YSdwT9Z2mP/EEJoo32JNxpL9ymf17VwMZALyH9Z1hgU4wUvfHAg1oxbJK/C0WVFyYsdxiBcaqSpwzFmLimnEtXrlypeZHXWLyAa9fv96wRds4YazBLAMxSgNoAePE1ZJDGVdq2x5Al3MjtE0MNpZzLMS2X/bywNKMqzTidhbo/MJ2ZdfU2XZN0YPwbme29KU4GQSnfu+LqxW/U1OmBE191/7Z+rpaxDV/tBI0WfHjSqppZ1gGl7jk5EUksnWt+Ke5F3RgGfrXT/4mAWXLnpar1lyuR10AuU6gm0qfuPlVtWXJrlsekIMuOsbcH1KVc8s+75RKia17u9s9kljBPAXCLAi/79S/6cSxWA65hHvSdLlFb/2N86kn39TJF1x1mQA0d8n+ipr9a+vUo2h7rYyfkkmF5FQUlvGON5V7Qp80GkDmPNRnO6YXadWyrX32u3nHvMMnyb90wipqUr8lNAHAbVKwyzKYBLTuvMMyFkynnup3HiyBlrsle6CA/q4KW5YvkvkLnbUz2yNBA10OiSOhK2O3DxkW6KONOzext7gYA2CxpsIwTW5k8wKr8c7k5uUlAfdncvTivo17sU0XRUwwQJTjiH2hsGv20ZZd2A/IhqgK0M15rVjLLyRbyP33328svZBi2X6x//HHHzfxyhUVFd31neejjHV9Zhuxx+2afbZPrJ37OTbaJFj1vATNC3HfFxHe8ZxLqrGR37Z8Rkgi615Kddg1+zqe/auUlLaovqzj3tCG7lE9FhW3SMeyx4ZWcYyVxrUvpHGTtWo1J44akJaOQPCCZZi8wO0NLVK3sSqdamO6TOPusOqvf6DGb7s/Qe/19Wkqv79GxtD+sIK2x+9/UdZ0qDukKo57/0CyQ8my6jUc4p/3PT9QMVcee/3xt9SlNCIVkjfg+P0K6uYrxdjLf3tV2poS3B0DVnDJwfL8lTJjZqNyRgz9WUOditkNUp630iXaSm+Yr926VB69NjFR1WVH6VMxphb3535aJC/+8C01GGTY3fso6F3ekQHAe+ELGIgFGiZoABdpfsgRnA4LdGVlpcybN08sCzTxr7BAOwUWaOJaf/WrXxkyKVigDzzwwD5gzdbBkgoRFi7JX//61+VrX/uaiVn97W9/K8uXLzeEUI899php08kCnU5f7DlYQ/JF34n/haSK8xLnC1C96qqruosSRwv4RdiGdfmVV14x+Y5tvDV1LaM0btVVVVVy0003mTECSgG8WGvRA58vv/xy475MfmTGQHyvzZ+MfgDWEF+R85e2+S6IjcZyTFn2I5YFGlCN3gdige6vX6ahrj9YnxkbC7HeI11i9btk4pR6tQIP/WHK2PKL2tQC3CGx2oQ+R/p4/1P9i+3eLrl5HcrkjHVt4JfjVH0oLmlR10olh1E2SjdL/dYaiSuaJSJ1TXORmYAZBGuoa7kS5yjQe6uxxNRDf3WbE/cbN+uyefNOqW/LMfoZqh5a1HU/FPZJqCERzjLU+mOt/MaV2zT3p7Ld67X2SmtI2vSii+iFiYXSiknZo583d0ZkWzgmnQqWX/wn4SUZcWpg1TNrpKWebLZ+mSkFkq9rXl6xCLP26bpUgjJLj7HPH/DJupc2OJtw9Xa8bpWc8qG3dDK/J/QjXYXE1S365A++JfH61elWcUW5Xa+slqZtAfnLx8bLrjeCEm7TdHvNPUvTDp8s/U6prH88T2I6sdWw3t3vOyPxosi4QO+hb8VaATMs0GLYlZ0pmZJVDMv1jTfe2M0CDfD8xje+YVIiOctCeoUQ+4tgCT799NPNNn9wb05mW66o6Mso/e1vf9uA+xUrVsjNN99s6sNCjQs0VmkEUE45Uip9/vOfN/uYtWdSwJ6T8yF7mgWaPj311FPd58T9eiRLXIkfsnLCMnlGrWzbkIiLTqe/WDoDmmJl0jRl2SbGqMndaRWIg8aiNn5ig2zbXK7u+GhRdwwixF8WquW3sCjBKhmrd7flsqW6UWfXE5MxEbXoLq8rlTkFTSbdB/HT6NgK2IO0Hm1qKd7QosQ6XfqmfktNIn2aLevGdbixWcL62wQEj1NW53SE67Y9HDBx60HNc9tZ2yDB4kwca/3uZmP5RYfYft5SC2aB+pQXKdOzkhgrGNasAwqK6yMaBuFQdH3mOnRoI7FZu12fGV3zBtkKd6crBMYVOqx/DeDV3zH/rPB7bqptsR9dv4637pCCok654uqX5JbvHKHXJZlIBrZ/USY7JyKf+sLzkl8QknjbTtfr0amASFfmgJZqvzzx1TLJKohK8YyIeANxaa32SdN24FXimvTo776zvtlZPbM9AjQw8C9gBHRwtHTBWiVhgbbLvffea4ANbMAwKVuwlQ4LNOO2LNBsO1mgyc8LSAI4WhZoyyTsZIGeNGmScSPGCklfYIBmcbrgWpZiywJN/l3iYAFkP/vZz4zl1LJAW7bpdPqCVRfiKOdi0wphlYUACzKpkhK1wOhbKTHDycJ5yBNsBUspVnIs3giWYSzAtq1FixYZC24y2zJWcMiynEKc8Wc/+1kTe2z30x77raAn+mqF4wiTHcQrI/2xQHPMskCzDQu0Hb+dLGE/ct1115lJA9IywWg90sUTTMT45eV3yozZu9TKoQyHA7rxwv4c01yrbVIxtwusKauGt7Dnux3pY/5P9M+bk7iGAGhTZ9RIXr6moTF67HrT63PShB5LxjVJcUmPe5+3dGKfkm7aUTC+SLywaXcJr8RYgte3FCjZS7a0ajqfzqjXrKv189sKfNfqccCyFdLP0I7bJVCUuCY7NC/17pY8iWicf9fcQh/VYEWPxjSOsCNbGtoTE4mxUFiyyjJssSireFx+H501q9K2qTXo7c6obApFZXcS+KVC6QRll81ILw0UluL63Pu+CPDNUjAcMBC4B/xSsbO5XfIKMrm9rRI9+VPNZlFJh1xz3b9k8tQmBbf9eR4pR4cemzK9ST7xuRcM+KWyJ3eybS6zVg34c3tfX53NPql6M0t2vpqt4DeAxrr1xDtuTlnm+dKtkBGykbEA7+EvIsMCPTAL9PTp043GsabaVEh28sD5VQBYf/rTnxq3aFIwAdBJDVVXVydPP/20nHPOOQacDsYojWv1Aw88YMiunCzQsEnjMg3TNG0BfJlEqKyslO9///vGOoxL8rXXXiu33XabzJkzpxsM72kWaKfFF0A/0sWTr+lSWupMN7OVdKhy7i6pr82Tpvp8nRzouembAmpty8lTV/SJjV0P3K7Rqb695YlrYaSP9z/VP1/ZZIk1JNxuAcFl45skHPJJc1OOtLVqfl91PbMvff5AVN3XOqSgi4Csu0/+oPinzOn+6MaNcTMnSkyBRG/xSLOSu7CkI1GN16Qdt0vh3Epp25aYYOxUwpyq5gLJUTIx2J6DGvJAKi5cx6M6eQA7dJsCZWfMsFc5GoJdINrtupy173RpbemZqEpHHzjy7/e+2ekUdU2ZkOYAfvtvzxhiNiZd0pGYFqxWF1U5eVE6xcd8GU/JAvM7xXsIS/BlVy2TjW+XyopXJ+p6nIagKRTQ3zXP85lzamW/Rbtk5uw6jRnuUo0e85QuGPN6GsoAJy1ZKBsfei6tKj5ldy+syJCIpaWsvVgoA4D3sLIzLNADs0Cj7pdfftm4G8O2jFXcSU7FcayqxCEDRslljBA7/MlPftKA1GXLlnUD4MEYpYlpxuKKpdnJAk27WIZhfgYAk3aJdEnEB8/U9DzE/BYVFZl4XyzfAOAMC7T5KhRsJIiGrGupzx+TsgnNZgl1ah5qPR5TyxCgDZdnm/s3UTvxN04+hryRb+129nlPb2cf/kEJbVRyF80ZaCUQjEppWYtZ2IceIQ3rVwJByT7o+H4Pu+HAuFmTJKqWx3cisYi6r01zt0cC+is/9ADZ+eQLaruw15wSCSrIZRlMqFEwb+ZgxVxzvKa6Vpo9DWqlzFN9WiQx8PDJZLtqy6qBC7ns6Kv3/EuCnW3Kd6CecPayHFAHGiISiMmy2x+VAz5yuIyfl7B+DlhljB+MtxHO0KM8nsmz5taZJZ2hMzET14mIjPRoYOExU2XboxEJa5q9gSUu+xypaZA0lVdGRpYG0rsrj6w+j7reZFige1ig+fIefvhhE3ML+VdhYd9YMcixAKPJAgO0U9JhlCaXLwKodgJtGKURe/zUU081LtmAXysAZ9IkES+M2PpOF3L2J7s02+N2TRlcYOzi3M+x0STxSFhC1TX9djmYFVWLb0jdzzqV3Kkf8KvP4Wg0IJ2v/bvfdtxwIPvwD0ncGfyXYtADgV/iWWM549QCPCtFTffsat3dJIl3i54XvKGNPm5yLXc0tg6t2hgsHdXwhmFrUStGAglX6DGomiEP6a/3PyKNgVqN7x3kR+5oudFXK3994BHzrHDsdvXmK797UlneO2VqnobaDHp1cvV6ZFJuzNRZ8dcMozYXT3yjPms7iPsd+q/b1KHuxn+5+jpMHnzeG7+X+dMT3jLJx+xnn1cntAtapbL5bxJrabC7M+sRooEMAN4LX0SGBbq3kj/3uc8ZSysxyqkEyzBCzO8999wjO3fulAcffNC4JbOfVEVIOozSpFsivRLxt+QLhjn7K1/5isBoTQz1eeedZ9riD1ZiBIZqzsmacx177LFm/3+KBdo0Pkr+RJW5GO7NaMirD9NhdlrrRTuUEXX968NsYGxU8xWVSW1ziU4GJLmNpzk8XE/r/QvTLD12i1W9tUld96yFcqgXZaJ8Vl5Qqt7aPHaVlObIdj3/hro1+4f12+5UYrHdKzeleaaxX+yhvz+lbuLtsjMroROsaP0JNFiAXwAzhDmvv7qyv6Ku2h/R1FDVa3nmiOT44zKrUD2HVFIBYX0iSZa+0c4vCifcpdWrY+3jr5nybv8T2/WqWgF0IiY5UiQNxZgJe60b27k8jdLuKBJrrpNo1WaZO61aluyzXoL+iPh9PRNdAF+/LyLTx9fJUYvW6gXrlfCqF9yhnFE0ygwA3sNfFkRGdrn77ruNqy+WToDYSSed1H02ZtVgIWa5/fbbDfHU1q1bDagj9pW4WFL1kIrn/e9/v/xL8+Qef/zx3TlwTz75ZEOsVFFRYdrknE7hpvXlL3/ZtINbL5bM//7v/zafneXYpi+wHVuiKGJfiUX96le/ata4I+MiPNy+JJ/PEkgl77efLdvykiVLDBEX7sqkN4IQq7i42FhzbVkYpdHtlVdeaQjBALo33HBDL/Is4n1JhbRx40bjQg3BF+OD8Xny5L7EDqSAIraYuGBAsyXCsv2yLNCkmkI3EHjRL9yxEcph9bf9Ouuss8x3hZUZwcLtFCYEcJ1nsamfnMdH0na0VkmsvOrmrLGqcQVuwwHBoTaNcyaOsCbxYjOSxrc3+xLTuNNd1SXS1p6lv8+hnZny23aWSnNd5hbeXFWvbvnqbm9cxZlM6B9o9NYy5TymXlwV2rxLmWZdLq07dxtysDaN/01XuAd0KMlYZ0wZoBszzLtWb9VViedB1BORrVnrpN3bovgDe3BUr1CcnROfIhKROn+11AcSfAA8j2tqEhwLti23rvHu8Ad7rsUsn8hcBbjjsmMS1N+7V2NaYXrP0fj0iTlRmakAWfnsuqU1w6id0EW73tt4xtQnJhCMVbdbS6k3KGOWGg0voW575pq0miIVpLJ/mo9TyxvlxIPflMVzFRBP3SWzJlfLvpU75AOLV8sBc7cqsaU+kUKa9pE6GRlRGui5s4yobo2+zlRWVhpgA6iyAogFtJ144olyxRVXdLNAY1Xctm2bnH322baoAahYGB9//HFDDgXhk2WB/stf/mJYoAHAq1evNi63HAMoWxZo2xCAmHYAvJYFmj7BAn3uuecasEYMrhX6QpuWBfr+++83YJycwSyQegGm99lnH4GMKt2+wAINYZVTIHtKJnmi3WQXYsu2DHD84he/KG+88YZhvSYO98wzz+xV3jJKA86/9a1vmXEkM0qTQxhAC5CGBRuAunTpUpMSifE5mabp79VXX23AMvHDWIsvvPBCw+Jt+3XooYfKZz7zGZMDmPRJ9IE8wnYclk0aki9cufk+AOBYsRFbznzQP0yMoAcEwq2RLN4cdQc3qNcjYSXOCGiaBPH1TjWTqv+mir4oUwfwi3hyE4yzqcq7YZ83gKXNI+s3TZC5s3ZIlsZL+1SXg0lEwUZ1TaHU1RdI2cKMy2lWfo6xmvGiEVC28bDGTSdAcOI6S63PRAIkv5annkdn6LMKclIXddHegOoSCcd90qLvvblq2UCL6ChZ+E1ztWL5BfwikL1kJKGBvLye6wkQXB3UvMBxv2TH8nStPAn6L+ztlE6Pxmc69IteC/ITYTdu12VWfrZAUOeUgALcCTkxszj3p9oO5PYOm0pVxhX7/KqHkKbhUSNlHEBbos8enUDA2yCVkFfdgN4G1b2dnPVnpyrqyn2enEJVpFWMPneUB2VqeYNZUipEsYAnV+tkZERpIAOA99DXgXUxWQA6pOzBsgrAtCRKgF9iUrEMO4VY0/33318eeughA1YrFVQj//jHP8waEJ0sWIYBmjZ9zqZNm4zrLqmMAHwAM8AewIqY2bffflusizFt0RcA7o9+9CMDwrBcAjgBkzA2r1y50liHFyxIMACm2xfG4MyJzLnuuusu0ybbCPrhXIBlp1iL6x133NFtleY4FtXGxkbJz893Fjfb6G7NmjV92oIFGtZm2mQSwAruzrg4Yw1GTwjAmFzA6NQKll1YpC+//PJuSzCTFDBFwyKNMFEAALf94lyvv/66Ae9MNjBOSLVsbmRbzp7jhBNOsJsmhVb3hxG44S3WtFDd5kpAsCahULIrv5I3GdHnqfNl2QBfPRCLeCTS2QN+Kesrn2qquPlP1rhiad/eIWvXT5HycY0yXmeTjVUjCQijx6gC37ASjO3YpZbfFn25Vl3nVU53s/rM2PPHF+s1mHiUce1hCeb9LapLf4LViHc/e6161GyUn0k/I0Uzp8i2pa/oy52GKChIawp71dKGdT1mLG2qMiPoNxTzmcXmUuZA7sRxiQKZvzJ/wWx5LcmVGSDc6mscUDsxfV5Uzsr8rlESk1JmUkVjgIcjGWb3hNY8eROUxCrhkWAAba0C22z14MrR2QTYxZxCgup2BXcapuQU2shIQgPeceo5aB8eaSjFo15z/hmJd+g0imeK7CUNZADwHla0E/SRxxYLIGzDuOXi7mzTAJF/lzy7CG67gECAKml5EABUpQJgACvADnfb5uZmw6BM7GpFRYWJbf35z39uAPJFF11k6tk/nBNLJVZXXKEvvvhiA2pTub7QF2uBxD35S1/6kgFvAHfSAFnX7aH0BStscoyvBbb0EbD5wx/+0FjNk9Mg2YmCVatWyac//WkhNzHAHt3iIpxMnDVQW1i30S9g1dkWEwIAYFigAcCc6+abbzbWWsAtgP/RRx81FnB0hhUX6zFC/DBWYpue6frrrzfu0jadEeOkn4De3/72tya22Lq5w3oNqB6t4tOcs95CjVtta+oeQiysL8L6ouxR0OZVVzQPrqj6TI2rJY4lFtGHbJfV11byBLMke/Ex9qNr1+MPmSeb/7zT6Kemtkhq6wrU+tOuDORtEgwQV6RWIgW97R1BaWrOkRZNj2SUq3/9wZiUH7HYtbqzA5+yeI4BbPYz7yU+Xbxdsy+8xrHJfv2f8r2FScDJ+7ubTAz9TTt2iay773EJNVlCMI+CXL8uHEXQJlrsKx6/TypOOqzvAZfuOe1Dx8kjDy2Vhvqee2U6qpg6bZJMmNiTfz6dOmO1DL/LucfuL2/c/5z+hrn20peswlzZ9/RD0q8whkt656uHWu0aZqJ7RgnA7eiZuDY/6+7feU8xs+XVie59PpS0070fPfrOnH3E2dL++G/1BX7wyRlv2VTxT5vvXoWN0JHrm2lG9qQG9t13X7ELbq/EfQIiOzo6TEyvPRc3dluO2E9cmok9tYzDlZWVpigWV8rihgsgPfLII+WUU06RFStWmDVWUSycuDxboTyg1sb+AtyuuuoqY821VktbNnlNP7HU0gYC8LMylL4A9BmTcwH8IVhFAezPPvtst1u4PYddc37KA06xbqNLFoSJBSuDtWXdm2F7drb1oQ8lbuaW2Zl4baz0l156qRB7zEQALtis6QuxwrBQ0yfWtAXAhYgLki3EMkpTh+/jmGOOMRMefAYw853Y9mz/R+M6OG/fFO/ACnbVQhkN+SXSEZCIWoajavEFHCeDX8Yc19Q/wQVLRuPw92ifJ8/apaFEPb/dmOZXbWzOky3byuXtjZNk9dtT1EV6orH6trTiUtkDPgJZESke1zvMYI92bpQ0xu9q/3Pe38d9nFsYC5Ze4gKdFl/n0PxKrnPAhZnJGHQy6bD9VY9O7SRv91x/fY6oS+C8809M3u3az8edeIRMmFA+pPFnZQXlf75y9ZDqjPXCR13zYQmqK/RQBffp/c44dKjVxmR5377nqGtM4v0r5QCZW+h5DPUtElBL/MKEp1zfg+7ck3vGZ8VTMM5Mrg6mgcIrbxmsSOb4u6CBDADeC0pPlQYJgATDsV1+97vfyXXXXWcso1g4AcdYLrE2AvwgyIKZ+LDDDjOsxAA3LJRYZ3GzfuGFF7pHgkWVnLmA5Pvuu8/sxxUaV93k+NPuSroB+IUMi3hZyJ1mzZplLJjLly8fdl+c7dvtwdIgoZsLLrjAuIkTvwu5FOMD6CMWSLM9WFtYkwGpAPk///nPSi5SI08//bQAeJG5c+eatY3vxf351VdfNWAY4i1cm3E9J3aZbdzH0ZOzX88995xpw04W0F9AMxMG6JJJh+9973sGANPGaJdAgT4tPQM9LQcZobqfBoq0fqRjkIJj/3CwYalMrKwzlvOhjnb24q0Se/uRoVYbk+Xf9/ETDBPncAYX0DjXJVf0hCEMp42xUof71sQSTV824Ntw6tGWavqzQHbGqcxqB0+t3/zhhzpRmh544xlz/sVnyCmnH2ubyKxVA+PnT5WDLjlW8VvWkPTx4Z98UnxdoRFDqjgGC3tKZop37qnqNtQTl572MH1B8c77oHhKKtOu4oaC3vwS2XTod8wka0gn/5Mlqt5vbWoQeDbvMnV/zmRrSNbPSPiceVrthW8hVRok3HkgSbKCNREQhjXjC1/4gmFrBowBmIgRhUAJ11msjTxYIV8CEALS7rzzTsM8bWNMaROXaYCedYXGIgsLNERYTmuxPb8FvxA/YTnGhRngDvgDDBMDO9y+2HPYNazHWEL7E3TxiU98wrgc4yb++9//3pTHko0bNhZzK+m0hUvzrbfeatyZiXVGrBsyFnUEq/L8+fPNZAIEVwjfBdZ2rO+IBbhMOkAWZvt1xBFHmIkKS/pFXDWW+QkTJhh2auriUk06JSYtmLxw5jTmOwIoI7jCU29ES2uNZBWFpLNBX0iSXJsH77dGDKpbb6BImY+ba8SXWzx4lbFcoqNRZi3ukKbaXGmuTY/4Bjfz2Yu3SemkZok3bhnL2kl7bPm5ITn1qPVy/2NzNf6378vIQA2dc+Iqycvu8SoZqKwbjmWFm6RM9Vnd1v89urce4lKU1SHlJRoKUbNbAilyu/cu755P8/eZJUufu0+WvPc00Xm/fm+Xfo23Xjx3gfzgJze4RzlDGOlJX79I6jZWycp/LBu0li8rIOfc9hmZc8z+g5Z1UwH/ibdIeNfrEt+9Ghes9IbuUYig76r+kzIWzFQKW/vqdvnNQ4fLYTO3yWLNCVyQFRKAb1Tfi1ZXjZOla2eIb3K7JPwNU7WQ2fduaiADgPeQ9ivVZRmA6IwBJo0OFkeYmgFENpbWMi87Tw1ZE8sll1xiXGc5hvswdYn9BcThtgvZFlZc0iAB5rAuUsZKRUWCBRqATewv7QFoAZIASIBtMgs0FuTkNEiUw+03mQV6KH2xfUpeO8FvKhZoWx6r9SGHHGJcv5kcgJGZeOBFixbZIr2AdH9tAag///nPy6c+9SkhxRPWdCy4zraI2QWY8h0AVtHj888/b6y4nA8mb2KNEVybmUzAKm1ZoIkldqZBwm07mQUaizAAmDRIzvRL6NSC51STE92DHSkbPBCzNZVHYaeEGtOzbpiu6xsg8cE5ZR0KgnUmWtvJiMfMIB9wwjpZ+9JUqd6k8dXETDtcna2OPEpG5FN33TkH6gRLRUNit4OJ0pZz4zpcXyeTC3bKecc1yV2PHaw6jenl1T8Q5niWxliffvgKKQ42S0Tv3cHivinR3KjLeGebAtqYuow3KQjO1wkF3J5ZkkVj/jUmuCi7Q8py2iXersfDmYmEZC1NnzFFjvYskrWRbVLlq1NNarhIVyHSIWXHAzIzOkkuPvmM5KqZzw4NXPC7L8q33vc/0rRyg7kanemOeJQY4js98v5PnyELT02EJTmqu37To27M/kuflrZvTzT3xmBgYC+uNs3Y0NBcKFO+8bo+r4Ou118qBcCW3akp455cW2EWnz5XfEoYGFJmfHvPHOHmjFTDcs2+DADeQ181wBSX5T/84Q99WoT1F4umdd3FQphKcF3GmgtoJg2SFViHsVDiwnzZZZeZ3RBIYQkm/6/TIgpZFOROlgUa0OdMg5SKBRpG6uQ0SLgNk/IIizDg2LJAp9uXdNIg9ccCzQA5Rv5ezmfFxtLaz871QG1RjnhjmK0Bmwi6BshawbXZMnnDfM1iBSs7VnjrJk1+5mQWaIArfUAoB5AmhZOTBRoLMGLLmQ/6B4u/FXI+j3Txlkwmm4K6pEUV0HZo7k99OOqsp73h9+2/vp3oYV9WVLKKOw3g0x+LePIzjLFxZdy00GLuwdtk8tzdsuXNCVK/q8AQiDFhEFPdBjXet2yaplmYr9b33B4ikyhsnRmRjT+5XSbqBMvEcc1y6SnPy79fmyVbNMcyJGzhaM9jDndn9fKViom1cvj+66WkoF1/jx7Z8JNfy/zv3+x6TXa+8IASiIXVAdonBcGw5PgbpKEzW1pCQYko67NHdUzqLpjK8wIhKclul6B6JCDxqN4D3/qnqCuN6/XoVMDrL66RqBIOzZepMjc6RZo9bRJWNmhSIeXEg5ItQZMZ+Dc/e1A+fP0H9fmRSSXl1J/drt5WK8+8vF1ylFeiQFVUoADOr/dH7qAhfSC1KBFji2YbeOWGP8sxnzlFcocRN2zPNVbXd577fdn83IlywP5r5PADN5nfc252z/OEcbd1JNLzPfNyhbzy2hyZufMXcsk9Pe+jY1U3wxlXxWwl88wKS0dn4jcb1UlXsjVYUQpQmTFN005lZERqoOfNYER2b/R1CoupFUARcbu4uMIwjIttOizQ/aVBgp3ZyjXXXGM2cZN2pkGyx63rc3IaJHvcuSbuy5kGCQZqCLnoP3HJzjQ9WDGRwfoyWBqkgZibaf+RRx4xjNhYzbGaEo/MxAH9BBg7ZbC2IA/DGk5cNcCW7wH3Zqz1uI0DhmFuZvKANe7eVkiThHUXC7Flscb13MkCjWUawG/TG1GOc6K/ZBZo2rXl7DlG2zpSup/e5O9RvekLiFqCfcF2CbdCfKUPTgOEe4/IpymSAgVhjcfqAWvhsF+8LgfAke3rJdzkk6AjPWB+cYcsOHyzMY5H9EUv0qnHc1R3avlNFlJLdazaKjk68eLRa9it0rJqtTS+9paUzAxKrnollBa2yQePXCFNrVmyadc4qW3MlZa2bCnI7ZBxyq5dMalWt3uYOztasqRxxSvSuu5tyZsz261q1N9uTFrv/Y7kF7RJfW2+6sFjAAbWXWPh1Uswoi94WDkgFEuWgOaxDj/yM4kefZb4SiclH3bl57UrNsmlR98gxeKXCZKluvNIUVxDHRw/Z7y12F+tE9G3fuNe+cw3LnClrgYadMPuJjlr2qdNkYBel02KKZr1GZIsnarYuJqGr//wD+UHj/6PArwUF2pyJZd8XvXIy7L15XVKWhyTZ1+YI488P1vmVe6WBTNrpETvmapWqdd75coN5bJ6U7nwWPIrltusEzhr/rlc5h13gEs0ld4wYzWrZcHWKyUneLQCYK7FvtcaPjJHTb5fIssPFP8BF6XXcKbUXtNA3zvIXjv12DyR0xprRwggwoqK5fD88883u7kxA8icgsszBExYcJPTIOH+TEzvnDlzDIAC+OLeTGolQKkzDRJtA/BwfcYNdyhpkCDiKCoqMlZoYoBxE7Zi0yCl05eB0iBhFSXfLlZXrOKWidmeB9dxLOGVlZWG5AombaykTC4wVmcM7WBt0SaxunwHxNnatrZs2WLAPfvIvWzZovn+iPu1gls7ABiQbOOGaQsWaARLOYRd9M0yeBNPzfLxj3+8e8KDuGL6jpeAbceeY7Stq1a1SKnjBc6jD8mgAlyWmFrTDAjW41gvSY2U/A6Cxa1mV7YUq8u5L2toxCajTVcD9bft33+TzpqA+HP195oEcNFZgIkDm1+5n4ZCSgIdXv+GBOf2xMX3U3TM7t79yGMSbW2Tmm0lMnVuVfdkQWFep7xn1o4Bxx3VSYTqrSUSa2uX3Y897moAHNn4hsRbGqR0XKs01ucZzwOn8sw1qeA3lXjU7a9sfJPEIzHpfOkfknviFamKuW7fj6+7S5/FHmnQ9DNZ+jJcqu7OTlAWU/DLrXRdvEVi6j3+h//9h5x83pEye8E01+lqoAHf+6OHdSIwKKH2kDSoxspUlxrk0A050CF2tha0GY7K6mUb5PV/rZJF78/kXrV6feKm+6SzmTiFBLDN0+fwa2sm68JklQVvCZ2WqccR4BehzhPfvS8DgBPq6P4bfvDzkq3P5y9e8G+57tYTNGVhVCLG9ZmMA8pzEojKlWe8INMmNEr4nzeIb4ESiWU7Zru7W8psvFsa6LHVv1s9cMF5U7FA9zds6x4L+EMATTwwiUMFGJ955pny0Y9+1OSdBTymSoMEWHOyQKebBskSYW3evFkgdmL9m9/8prurQ+nLQGmQBmNuxrX7vPPOk/Xr15uxEm/LPkAr5FFsWxmsLazDTDwQn4vebFszZ840TNCAXwRyMWbinSCW/Uw6kM8Y0GuBuvMFhjL2O2Mbscftmn20bRfnfo6NNql68mWp3lFsXEeT++5VwIul16c5agF1yeCX8jF1EarZPVnql72WXN1Vnzuef0gijUqY0e7TSYOhDZ3yHVXZEm9tl46Xnxha5TFWuu7JpXpRxZRILE/am1UnvA2nIVpF2tVK3KJgL65W9Lonnkyj1tgtEnrtcYl3tKq7fVRK1JWcOOn0RFnd/VEpKNSX67DyAqgbdUbUlbS1Q156aoVemokLskpCsk06pDkekZD+gDuUiKhWYdvb0trNud3e2imP/em5jPqSNPDPu5414Nfu3q0aUwpAadOlVRedejGf7fGWxjZ57PfP2o+uX7fWNhkSMaciguo0NF7dd3Px5NLfOgvb5Qp+A0nIYPf6ndJWlwgfc7bh1u1443aJ7Ui8vzDRetNVD8uJh6yVWVNq1cOoTo5YtEm+cN4zMmtqfUJFUSXHWveYW9U1YsfdgyRGbBdHf8dSsUDjHnvHHXeYwQGMrKWRNe6+yWmQAHHJLNB///vfDbEW1lIImQZigbau0FhMUwEwC34tCzS5cD/2sY8ZF17SMO23337dKZmG2pfkb3Aw5mZcuiGpIoURIP+nP/2pAcMQWJEGij4uXrzYNDtYW8Q2I1i116xZY3L7AmSnTZsmhx56qIltpi2bwxeQjxV+k7pDA7YpC4EWYlmgcYemf0xGYBUnhRUWdyvoEkv6LbfcIqS0wrp8++23G6AMWHZasKlDfPKOHQlLFbHDI11CdQ2yvaVciktbJVtdSR1DT6vrG1ZPlFBbXBlja9MqP1YLxRp269A80roxXwoXNOpm6gmD5PEDfkO1QemsIqWFzjrv2pJcxFWfw41dhGCqy00rJ8k+h2zUe5w6n/X8JPvoA8tvSPNVb3xjavexsE4wulli1ZuZnTIqGD+xSXkn/NKsLpFco/2JVy2/uD5Xzq7qnuyKNVT3V9xV+zev3SEBZSSWlp50b836e2XpTyJqvXz9hTX9HXbl/o62Tp3c6gu+sPiypBSdc1jz8oaUh9y4s2Hrbp2Q7hsmg5W3zDK1ZQAAQABJREFUWCerBxPq1m+tkdzSdJnhB2txdB+P7VTw6+3RZ252WE44ZJ1ZUo6ss1liW54X2e+slIczO98dDWQA8B7Su7UCpsMCDfgF9OK+nCy41H73u981Lrc2DRLESLgyp2KBxg0agQwLAEw/rKUxmQXapkGyfaWe7UsqFmiIuLAkE+MKiRfgfCh9of1UYlmgIaTCJThZANhYbll+/vOf9zoMWAQYWwA8WFtYcJHGxkbD6mwbI2aXPMmwadMWQBYBuGL5RnDFRgC8CP1BPvzhD3enQSKel1RKTEA4WaCx+mNNvvLKK00dGKpxv4ZYLJkFGgBNrDGSanLCHBhBf2IhdXWOeWXl8hly4JFrMb4NCDZs12E33rBmojTUFopHGXjDTX1famxZN6zjocSLcVz10vhmsRQtVOc+dRt3PFf7qEE9KSWspGNtWzWOsEvize4GbvFw4neKOuJcl8/NlClzqqWoTH+vCoR96pVgBfd7ctE0VOfLTo11c0pMJ7vcLLEW9ad3yNTpdVK3OyS7qwv1maJ0LqpbK7g8A4sLi1tlwqSGbvDL8XhHiy3m6nVjfcsAUwf9q6Zht7vvi8maaaprEb/J5zu032dzg8a1ZsRooKOxtddvdKhq4b2ksymjT6u3eLtOunZNFtp9g63jbe6e8B9MP+/G8QwA3kNax+0YSYcF2mktdJ6eNt544w0DdgGfNg0SoKk/FmhSIDnTINEGALc/Fmjcoa21kXPTF0BwfyzQtAX4Pe2008ySbl8GYoEGUH7ve9/rZkjm5vrkk092szKffvrp5lykHiJOOFlgw7YyWFs2tteWT15btmes3MRR41JtgS7pqrDW/vGPfzSx25YFGp0AvLHWwqBNH5h0sBMLlgU6Ozu7W79YlLHsI7ac7QsWbr4D5LjjjrO7R+zan5er1rMOHYdPlj09T2Yt2K4vwm0K3NTtuecdubv/EWXnhBlx4+pJGlsIuY5ed8qUnTW+rLuMGzc8eQosOrqubyUPAwRnlXdI9gQFxqALdevDhRzwgUUzFvJK+45cA4Cd+vKVT3F+dN22V9OcxfR67BGPbF83Qaq3hKWgpFWJsTrEr7FakZBPWptypLkuT7f7Pvr8+e62bvjKpvWxqJWWtRhPj1YlCmtTd/GwErP51d05R4nZ8vJVrw5iO6t/T0Gp3XT1etz4omGNf8LUjP6ciitRPYZDPZNczmMDbZdOyMRbWv3kTyi2m8Nb6/tN/vh32Mbwzjwia3kKJmpaC81+ka4oUYqnuCLd0plye0kDfd8C9tKJx+ppMizQdUIO3IFYoEkhRKonGJJvu+02WbZsmdx4440mntkSg8GeDfg966yzZPbs2eZyIX/vXXfd1W2hZedgbVnQiZXXCS4htsLq6mRkvueeewxIxXUZ8Hr33XcbyzqAdcOGDaZ/nHMoLNBYd0tKSowLNJMSiPOcfMbqbGU0WIBzZ05XF9yEtQir0Lo3p0lOXoeUT2yUkvJmfSlWVmIFbFh821qypa6mUEmv9GVQgZwVWIvzZlXYj65c+6fMUj3u6hm76qezOkeXbPFpiimvuqZ5/OotwgSCxgnHFMD1kSzN7TizN5lenzJjfEfO9KnSnMJ9OaypKep2FeuSngKyZ0xPr+AYLeWbvlDZ7NStPtQ7DMOrXgkFOonAko74p2WIh9DTLCWy8jqT1aahPFL3vP+UngneNKqM+SKBoF9m7jtNVr74dtpjDWT55X2nZViLrcLKZk2SmBLUDVeYoB+nbWQkoQHv1IPUAjyESZnsIvHNOiqjvhGmgRT2mhHWw1HWHViE7QLowu312muvNazAuPZaAegA9lgoj7WX1EOAJQQWaMTJvMwxyJqO0fy1EEGRrgc3XeJWnULblgUayySu0FiGyYPL52ShLC66iGWBBvRBZNUfC/RgfYEF+utf/3qvBSIpXJiff/554xpM+5yP2GbIvCxAxKqKpRk5++yzDSszzMxXXHGFybFr8/Wm01ZFRYVpB6sxbbDgsox+0SFkWAju6FjZyZuM+/KECROE+GILVokHxvUasSzQOWp5sizQ6NVJoOVkgcZaDAs05TnnaGeBnnD80YLVzSntrdmyZf0EjV+bLa/8e568/K/58upzc2XNG9OlZqfOHDvAL/V82VmSP7vS2YTrtnMOP01ZIXtcmXsUoMRYbX4JNyjr6e5sY/FNCX61AhMJ2Qe8v6eqC7fGnXC8eHXC6p0I13PZCSPf++KdjHGwusH9j9br6Z3NiXtyiyTr4JMHO5UrjvNcPf/qkyU7L32m+yxlOj7xnMNdoZ+hDPKCL58meUW9nzkD1UePJ1921EBFXHXMpy7ks49+j2Zm6JmETlcBHp3EmXPMImXXTzEBm24jY6wcbM6+/c9ROu30njuewininZH5XY+0yyADgPfCN5JhgT7aAHwIrSDhAggedJDOoDkEwEwcLQIQxe0YsDh58mTjMsw+jrO2oDadtgDWWIGpa8H/j3/8YxN7zOQB5F7I8uXLzdoCXvNB//ASg2RYoI0azJ/JHzxJvX+G/6LsDfik4ooL9WHs7ttPzhGaFiGY/stxzzfQtaVuVcEFh4h//LQ+h9y0Y/wpJ6m1vMeLYjhjxyW/TIG0m8VXNlUCCw7TWZXh/y49WbkSPDADgO11dPHnT5eJU8vSBh43/OwT6lqePtCz5xnr68M/eKAsPHSOhjKk99z5+LfPkbJJCWPCWNdNuuM79ktnS1bB0K+trLxsOfZLZ6V7GteUCxz3TfEU6bPXO/izJ+uSv73jyUXXKHYvDnT4T7q92MnRfqqBWKDvuOMOk2oIUEaO4HRYoCsrK2XevHliWaB37drVDR6trs444wwhrvVXv/qVSR8ECzRW0OT4U1veyQKN5RZrNMARN2XAIWRVjz32mGnTyQKdTl/sOVhv27bNWLltzLQ9BsAk9paYW1yo+QxAhYSL+GMs0V/96lcNmzNjS7ctrOGXXnqpsfj+4Ac/kAcffFAeffRRY0nGAg2rNEK8L3LzzTcbkA6hFVZhUk8hWKWTWaApA7AeiAWaPMNVVVVy0003Gd2jf8boFOKlKcPS3/fjLP9ubxMDPPWUI3VyoK83weB9i2u+vLhMOfO0wYuO8RLe7FzJP/ca8eQOM/ZUJ4gKL/7vMa6lwYfnV6b1yRddKD4lnRuOeNQLZcrlHxV/QSI+fThtjJU6uR/5H70ehx87mXfBV9Vtf/AXwrGir8HGAZi9d9n3lZxN73s68def5Bflyo2/+pSccHbiedRfObfu5zn+3b//l0yYPk6ZtfsHwfnFuXLJDWfIh648zq2q6nfc4+dPleOuO1fd8tO3AlP2gz+4QsrnuptnIpVSPcFcybpas6qUzTVpDJOdK+PK6yG54yXr08vFk9ebcDFVe5l9e18DGQC8h3RugQss0HaB3AgGZXLpYok86aSTzNks8zIAi4UUObj/EuOKSy31AIgATcu8DCP08ccfL7j/rl692rAXQ6xkraGwQCP0A0sni3V9/s53vmNIlmCBpl1rCaW87UsyCzSuyRBxUZ4xPPLII0PuC+0nCyCXlERWALlYdUkXhMCQ7JS33npLYIumz5QFiFtdJ7dFPSertG0L5mrAMzG/AFEEMrAf/vCHhsyKz4cccggrA3JJewTIJiYYKzTCuVgQywJNGSYKsF7j1pzMAs13Dgs0ccx8b7jDI7Zf5oP+4XvCLZvFtmGPjdR1+XsqNEVVWLs3FBBMWY9MqdA8weoCnRGNBz/pIsk+RO8LGjM9JNFn67gb75LAtLlDqjZWC0+94lIpPHCxWtSHQEyiyqD8+A+eJlMuumCsqmZI4/JPmSP5n7hFFTO0V4O4xyc5p10tWYecPqTzuaFwLBqTo485QHI8fgECOxe0nKW6Vru55ptPhNi4QSfDGaNfXXD/sPaH8smbzpOyKSWSX5Kr2YCVHFCXPJ1AmPWe6XL976+Sy79+9nCad0WdAw/fLAv3qdan8ODPG6VHlX3mVMnC/da5QjfDGSRebL6T/1eioVIlYlSNhZUtX0ODo52EMQXFs+jT4i2bM5ymM3X2ggaG9pTbCx0araewFk1YoO1y7733GtAFoRJMyrgAI9atNnmstGFZoDlmWaDZBiRiwb3ssstMfl7y2ALOLAs0rMoIbQAQAdSTJk0yMa1YIekLoI+F2VQr9AUQbFmgsbRSF7D4s5/9zLRlWaAt23Q6fcGqCXGUcwGcWiZlzk8/AZXss6DcgtsvfelL5vxYnQGI9JO8uxBjQU6FONviM4AUAGnjp21brIkXZjLBClZmJxAHpGIRJl3S/PnzjZUaq7hNt0QMsD0f7dn0S/2xQHMeywLNNpMVFpzbfrEfASST5orF2afE0ZH5NxbIkZLcJinOb9PrCXKNgR+o+lhQy29UppbXajhw5rbj/FYLL7lWAgXqFWD06DySalv1rCloghMDEpy3OFUB1+6b/6ObpfCA92qW1Z7720DKiOgMfclR75eZ12as6E49dfomycZ15SmtGs5ybGP1IC91rRLd1Xe4m0QsWTd8jmiKruNLLpfXn1kjvpBH5vtypdKXI9N82TJDl3m6PcGTLR2NHfJfp90sj939bKpmMvu6NMC7y9mfPUl+ueybcqauSytLZO4hs+QTN50rP3v2RnnfKe/N6KofDUS3/F1iy6+XhQurZVpei2R5lWjRPLedz25Nw6fPao5Ny2uV9yyqluhrX5Po1gf7aTWzO7RhvUQb2yXaHJBIfVAidVn6OSjRVo90rF6VUdAI1kD/viQjuNMjuWsZFuiBWaBxbQaQA3yxwL700ksyZcqUbnBqY3APPfRQ8zXDEE0uYED6nDlzDPBcu3atOWbb4gPA+Jvf/KYpZ12MbVtYr0mzRJ5kCLi+8IUvmDYpz4IAcHEXZ3n88cdNfyD6YkIAxmgAryW52tMs0Pvvv7/pA39sf7p3jNCNNY++JUUKNEoK2iQ7GJa65jwJRxIufqTtUTSniz5MuyybRQqUC3PbdSJDlBQrLFNq6iW3PBOjFY9GpOYrl0hWbkwCqqOO3ZrSJ6K6w33KCeTQo+7yq9U9WNIhoRa/tDx6txScfOEIvUL2frdikai8+IZODjQVyZS8JvGpzlgc830GsEX1+mTZ1lok21fGZK7eWyATy4hOSup9dNV/XatE0EHZXVUgJaWtGu+vCJfLzzFvBfBFYppXuU1TJNXszJe6234jJe87RPLmzE4cdPlfnllXHf0NE/8bCUWNNmrVFXqCWjKZ8EWF/MobdELVys1X/lrKJpfIAe9fYHdl1kka+PMvHpNbr7vHpEbqaO2Umo11snX1DvnR5+6UP676gUyqGJ9UI/MxHmmV6MuJib7ymS1SkheSPJ1I7dD0hK1RJVw0zxslQdWwpjxfRLI1pWEgOyLllS06i6PgTut6JylJnn94YSZj9Rto+ffDUvP96yW31C9Z+aHueyT3RzJh1D3yhITaPivj/+fHY1UFo3pcjkfaqB7HiOm8ZYBmnWGB7ssCDWjFSnvhhRcaqyyWbIS0SIBMXIkBx88995xxvb7mmmsMQLZfMC8OgFWEturq6oxrNKmLsJ5iKQYM27Zwm77zzjuNezGM2kuWLBHioU844QQDhi1Ypr1Vq1YZizoWWvqFxR4LNm3BCp1hgUZLOtlQVSdrHnldIvrwRHKyFNCWNah1t17GFerDNb9VCnX2mO0JpU0yfUKtWooT4Desdaoa82TFLxMu+6YBF//peHmpRHZtkY46TX2UE5GCimbJn6az82UdEijqFL8+VIPFHZIzvl2PNUne5DbxBeKaPikujb/7gUSbEjHqLlZh99DX/O1ZadlVJ1WtefJq9URZ11Aq1e25apkMSovm/WXN57X1peZ4dWuu7F61Wd5+6MXuNty+sesvf5dIs770qjQ0FEiH5v7taNb8vx0BXXyaP9lr1uEOv3Q2K0u5Hq/dnQhfiep9e9P//tLtKuwe/7MPLpf1K7aY+F+7s02fX9vVKtygQLheAfImnbRxSktjm/zos3c6d2W2HRp4/uFXFej+VtOeteq12cOl0dLQprnpw/KRuZ+Xta9tctTIbKKB6NrbFcgmcs6X6DOkWBePAmCA7rhgSCZmd5qFbfZxrFDz0RdPbk8oMKwAet0die3MX6OBjlWvSs23P2O22zS3fEdTlpkQ1J+13hc1Bd9mzX6hU1xtzz8hDfdm7osj8bLJAOC98K1kWKB7WKCxwgI6iY8lNhp3Y0AtMc6keMLFqb29XYhXxm0Zay0xyAgAFpdjmyuYtihLDDUW3uuvv96U27JlS3dbuIn//ve/N67g1dXV3XUpA5C2Mb6cn3NWVFT06hcu2MQQ0y8Llp0u5Jww2aXZHrdrypgZfx0na+d+jo022fav19TSHpeNu8sEQGvFrw/OgtxOKS5oV/CrFl/NDYx1WFVnRIcunWG/7G7Ilg0PPmOruXrd+sT9Em9t0ncTBRj6AMWd1JelLyalnZI7od0A3pzxHRIsConXnzC7tWzN13Kqd7Uedyx/2tX6cw7+lVv/JuHWRK5a/ZVJQ6deZ40lsrq+XFbUTjBrPjeGstX6lrgoQy3t8vKtDzibcfV29T8elphOICIR9eioqtaXOLWWRzQHdUTzKofVMsw6qhMKUb0Ga+vy9R7cFc+v94TGZa9ILInkz60K/dPPHpPWpi4A4VBCSLcb9C25SfWVSqo275YNb21NdcjV++qqGuS/Tv++XosaZNmPRMJR+dZlt5rnbD9FXLk7vkUnnKOJ3zXP48Mu2qDPEI8BuskK8QWiklsckqM/vrb72U3d+Oa/JBd19ef622/uNf72hlxp2FosDVtKpHW3EioaTzhdhTul8U//pzHCCf33qpT58K5qoOft9V3txtg+eYYFuuf7heQLBmvYlSGDIs6WeGGA7FVXXWUKYtmFEIqYWay5ljTqiSeeMMCZ3MAIbeEWDcAl/hdAC8DE6mvbohyxu4Bfu/3LX/5SXnnlFbnoootMzDT7IcHC0kvcLn2CeIvzUvfqq682df9TLNCm8VH0Z8ezav1VoFHVVCi7W/L0RbgL4Q4yBh68r2+dasBHqLFF2msbB6kx9g93rlzWPchWdSMNqVUNEJws7MPdtGVbnnqkJYie4u2t0r5saXJRV37ubGqVho07hzX23au3KLDrsSYNq5ExUCmuni9tGzb2GklIJ6y27yiRdrUAY9mI6jXIurPTLzVq+W1uyelV3qP5RltWrem1z40fokp89eZziVCdoY6/Q6/FFx99fajVxnz5h36zlJnkQce5Rd2hN63cNmg5txSI68Mj3rS+13ADOsl61jdflTmH1Uh2flj8WVGzZOn27ENq5ITPrjbhSs5K8aa3Vf0pHk7OQi7ZjrU2S+eGlUMabcdbrwypfKbwf14D/v/8Kdx1BsvGzKgBdbD/AoCdLNAcA6jBAJ0sgLrvfve7BphhlbQs0BAkfehDH+q2WJ588snyox/9SC655BLTBOfFImoFKyPkURyHBRpCKyyc5557romTteVY05dkFmiYjWFOvuKKK4S0SJdffvmw++I8F9uM78YbbzTkT3yGcZpzlJf3UMVfe+21Qjz1rbf2zOYCUBmzJROj3+QUJtaXvMEsCH13tsW+zZs3szKxv6yxIp9+eg9jKUCXcwKm0Rn6o1/f+MY3umN/k1mgKUufjjjiCJMOyTI4Uw6rPzHDEFwhCxYsMCzQf/nLXwywJr+xFcZOLDQC6MbdeiRLy46a7u6t3jlJt3fJ+IJm8fWTFgmAHI765LUtUyWk8UaIR+Pg2nc3SM64IvPZjX/iiiTiHb0tRK3bC6QzJyxZpR0a7xvRGXr1GlDQEVKCjc46jQ8O945VjdbucqPq+oy5taZB0zH21k2fQv3s8Cpoa6tplKLp4/sp4Y7d4fqGrhRGCSu6HXU44pfqGtz5NKZavTxiavlNxPnbEj3ruAK/cFfquJ697ttqaWjVuPLh2RfCnRGp2to7G4L7NNh7xDUbquTP3/6zTsAMDsDCHSG58bDr5P+qfq0pkzIpuaRTryWvPnd7e9trOiSRRSdvN0uHckoQlJ5d0L913bQR0pCbrAxbeaS2Sn/fAVSWlsRCHRKp2p5W2UyhvaeBxNvo3jvfmD1TZWWlAS/kmrUCIzNA7MQTTzRA0gI3cs6SD9daMikPYQYWRgiYALukILIs0IAmmJdJg0QKJFxuOUa6JMsCbc9ZUVFh2qE9ywJNn2CBBvwCDl9++WVb3OS/pU3LAn3//fcbFmgYp1kAoYDBffbZx+TjTbcvWHWJz3UKcbWASiy8jBFw/61vfcucm345BfCIDkhJ9PDDDxvCLCy21LVy3333ySZlV8YFmlzIyOc+9zkTv2vL2DVgF7IpUk3RN5iuL7jgAgOwYcbG6rtw4UIzWcBxiLfQCd8VgBbLsGWBhqDrM5/5jAG9WLPpE3HF1hWaclihGeOOHTvM90FOZnIQI7ac7Rv5limPWCuzPTYS1179Dp2yeudEjestkEpleM7TGCIm6Xkw6GUjMXUD2lFfJNvqSyQS6wEogD9fUjvONt2wTQoFo6ykwUbalU1ye28dJxXp/pjJuZpQBdfSQK6R3QpLsRHpDKvbeeZR6AnqNecgZOqrKiUP04msAUUtRORVdrv4g+oinhTfOxSdBLOHls5rKG2PtrJ122rlq/tfo673SQiuv4HowwdCvB8c9zX5wj+/mgHBXv09xgfWXXb+AMDX6pk2PJn7JOowz90hWMN5h87cF+2FNHLWmat5D30XWHqTBaADMCOXLWDKkigBfollJQbWKYA+QNpDDz1kwCqgGvnHP/5h1lhjkwUrMUCTtD4IgBD3YgDeRz7yEQPMli5dKrfddpuxEBNHi1uxFfrCjxPLKiAXyyWpmACmpAVauXKlsQ5jwUTS7QtjIB+yU+666y7Tpt3HeNesWWMAqd3nXDMGrLpVVVVmNxbXww47zFhW6TfWYZsaCpdlK6+99pqJH4aIjEkHvgfYlZlcQBjvfvvtJ+vXr5enn35azjnnHGORJuY4WZi8QDiXBd+08/3vf7+7X/QBMG9ZpylHKqYvfvGLZrKB82OpthZ6W86eCyBuhf6MdCmY2mOpt32tb8uT+s154tf0CdkBBRRKohFSy1F7OPWLXExjtXLK3Gv9tXrz+DUjI0GBwxJl2c7JgA1U194ekU4lxRkEnqXUcqg9ZOpr1Jarxa/343iU3N7vQCJK3lZa8g4aGBtVc/OzJaAg2EnUlO7IcvKyZHJl33tsuvXHUrmoEob9/CyNtdTJ1DyvRzSJnibpGViYfM3WWdhda3bIM7c/KUdfecLAFcb6UX+BjlAV+I5FJ2z9br9LJpTonzDFeE6mq1IAc3D67HSLZ8rtJQ1kAPAeVrQT9GG1xQJIap0bbrjBuDwDKhHYinFLRoh1xeoIUIXMCQFAAYABrIBEYlwBeVhvIW6qqKgwRE9YKgGlWEedwjmxVGJ1xRX64osvNqAWt+FkoS+AXwSXXnLwAt4A7rgFn3TSSebYUPqCRRcLtFMsgGQfVlXSIOHya3MoO8tybizcxPJiPb300ksNUzRu2YBPADB6I61RsgDgWX77298aCzwxv8QPowdii1999VUDiBk3AB8hDzCuzFZIdcR3h2WWdEh8F7afEGMRF3zaaaeZiQ3ItyhnGa0ZJzHHgF76QH2s1AB6ADlM16NZxh8wT1bf9WjKIWDlbekcHIb4c4ISLMxL2YZbdsZadmvclbKXmvmoYbygqHu0rzVx/bpFZ/2N88FbHpK2iFcK/ANbOpLrczts1Wv2oVselktuuST5sLs+xyKSq6m4WpTkatiiADqnsO8zZtjtjdKKTLIe9IH95Mk/vZBwhxnCOLwaHnLwce8ZQo2xW/SNh5ZLvVqAcSkq1MlCb0gB8CCXV74+fgIKlkNtnfLgt/4kB5xxsBRNdO+kjEd9nT3jDpB41b/e0YXiKTtQQ3IGf7a/o5OMksoen1+yZ4yX9pVN2uPBn92eWJsEZiaMSKNkiK7o5vCCVFyhmuENkthSuwDcTj31VAMicbF95plnuhvlAWnLYanEpflrX/tad7wpgAsB3FKWtEGAQgDcKaecIitWrDBrYosBa7g8W6E84I7YX/bj4guQBOxZa6otm7ymn1hqaQNxuuQOpS8AfcbkXKwLOCAewI7F1e5L7gcWadyLsWLjfozQFjl5iefF6oobMsJEAu7FLIwbOe+884y1GWs38dGAUvQGwEeHWNrRjQWttMdxu5CbmOOAbPRIPyHZsmv6BcAlZhv2aoQ6COeg7jHHHGP6wGcAM31j2+rWFB6Ff8Jq7X5nonGtnb1jX99Ze6OzdnTlY5JVrK5n5PgdhvAT9UW2S6xhxzBqj50quJq+9OeXZCeu4z23wbQGSPGdbX55/t7nzW82rUpjtFB0/YsyTtOeeH1Dm0Sw6vB4YlIyvkUirz9kd7l6fen1Z0heQc6QdOD1eWTxUQvUAjx+SPXGauFn71wq7ZoaCvHpDW9m9uCvrJWOMoRFvP6PV8aqetIel7fyHAmnMTHdX4NhJb3zVn6kv8Ou2x9v03CvnLfUFTq9Z3fB9HaJb3pnExCuU/JeGPDgd5O90ImxfopUaZAASPfcc0/38rvf/U6uu+46YxktLCw04BjwhbURIE3s6s6dO40L8LHHHmtS8jz66KPGOoub9Qsv6Exzl2Cp/OQnP2lAMnGyCDGqgLzk+NOuKmYF+IUM68033zSkVMSmYsFcvny5AYLD6YuzfbtNTC/gFhdwxposuBNj9Sb9kFOogwCCsYITQw34B4SSA5jFCu7m6IEJgg984AMm9zD6RocAZazAAFEs66kEF3X6geXZfn98BlCjJ6zKkF6hd3IWI3aygP20zYQBumTS4Xvf+54BwLQx2mXbEy+J18NLcno3/1Tj9WvMZf3qjakOuWZf5I1/qKt4iwSU7Go4IDi7tF1n5DUuc+1S1+gs1UDXvbBO+Vm80qrxqY3KWjyYhci2QbkmJRVr03r8Xje+4u7rMbzyKU1dtluCWXo9DuO37fXFpXxSnYQzANhcYrPfM0OOO+8wyc5NHQZir0PnOhaNy2d+eJFzl2u3Y0p4teGFNb3Gn6cTBAtyvZLN5J8e4QXWrgt1Y788rz6bEpP3VAxreMOrf0kQTPLZrdLU/h7pbNEMA8N4ZFOnoyUgzR0ZrwR7/UTXPi6+oOZQnqfeCUZSKVaT7SlpYFFFg2Tl1knktXts9cx6hGgg4wK9F76IVGmQcEWGJMkK1kTIk7ASkvsW8GZZoE844QQD2nCdxdoI+AMMAv5uuukmufPOO42V08aY0ibAjnhS6wqNRbY/FmjKW/BL/CwWT1yYAX7JLNDD6QvtOwWiKiyh/YkFkk6XacqiI0i0nORauBcjxD1jWWZigbaZJLCCSzeWWyzFTp1jJbZx2basXWNt5zsgPtiK7Rcu4ZCFOVmgmRyw/cI9G+ANm7OTBZo+MWlBPmGsyVZoC/dyBDf3kc4C3VZdpy8ZiZzGidt+zwuHHdNAa+KEeRLDAu1exzQlaumy3OaOb5XGzTp5Y5SZhi5V98FCvYYKNXhYGWNjTdUDqXvMH2uqadK8tAmr5RZNERXU+PN8v5IxDaBKLMXNmud2U1vCuwQrcmN145jX1UADjNVtNazjFfvskrWvTdW0W8CLAZSY1NjMBTs07jUqcXXtz0hCA1/6xeWyefV2Wf70qgE1qXdTyVLiq7tX/iBj/e26eDqa1GqWAldk6aTffAXBHfob7tTjXKE5eqkGdCOVd1XjTmUudrm06z3yzQfmy2Hnvy6B7KG5yXAfffmv89XQ0CjF812uyK7hx6pW6sWnmS/0Na5sYY207MiXzqaedzqe5f6ciORPaZGgppZCYjtXdNXOrEaKBjIAeA99E5Xqsox1zxkDTFxoTU2NYWp2pkGCWRjmZaeQMomFFDy4ziK4/MLyDCgi7pU0SJBtYcW1aZCwLlLGSkVFggUagJ2cBglX6FQs0FiQU6VBIlVPMgv0UPpi+5S8tuCXtugvQNMpNt0QFl10SjlieJkgIObZphty1sF9nNy+CGNxgmeAM/uOOuooueaaa0yZ+fPnd6eQMjscf7Dact4Pf/jD3WmnOGz7xffDZALWXyYiOBdWadsvykFKlswCjUUYAEzcszMNEhZ2a0VmImKkS0xZc3koQnSVYHbmLUV3DCiJNxmfgl/qIhG3515VwiDEoy9vRRWNQh7gCG5qmmYmtagONe43u6hDsks7E0VgogyP/Gsm9Xj2zF5IrHq4DTzydmuOlCkb+cRsvU71FGo0MtccL9NqYDPzDLs0r+3uUI9ljvq042rpbDXDxwI8/4AtsnJZpepNU3XF+7seKZ5IjTRtjnIe5HfpL+JyPTouIgDZ9/72X/KB8suV7C4BPJQPtrsEwBdRLctxlxyWAb/dmhEJaTojn8ZDpxL0mqOH0nEwD4feIbFbqg6Msn1RfdY2VefK0l/OkeM+u8ZMLNjncH9D4X4ZbvfJ0ttmq4GE/PPufs700lMoca9kn09zKhdVNhmdRnl+q968ah3GI6aXRLqe2b12Zj68mxrIAOA9pH2AKS7Lf/jDH/q0COvvJz7xie54VyyEqQQgiDUX0EwKICuwDkOohQvzZZddZnZDIIUlmPhWYoitJLNAf+pTnxJnGqRULNC4+yanQcKtmJRHWIQBj5YFOt2+DJQGCYIoXIKx2ALmeZhB/mWBv003ROqmZcuWmaERB4yFGwtvKjdu+gjTM+P/yle+YmKm0blTIPoi5y5EWlhrnWmQnOWwrNMnCLZIw2TF9otY7mQWaGe/KMdkRjILtLVKJ/cfxm0ryW7fdv9IWmeXF0vLNs2Dp+9xWHNJdRTrfknueblL9DnxEFCuYwOY7UMX193c8W62/yooK5yoKZRXGzWhl/zJLRJuVebY+hy1aOqDtCs2GI2iRX92RHI0P7AvK2HtNBUD2eIp0nZcLMUTijWlVu8XZcBtbSggeWoJztGJmoAuYZ1YaFerZqsu6pzWS2O8aNOOm8VbVtE9fH8gJgsO2ijV20qkrjrhreO0CHtVn1yfeYUdMmlGrbr59oAMT35PqrruBl28QRxw/vh8qVIyJ42kVJddIDB3xAT8VR8OySnI0tjfhS7WUt+hF44vGnZqM2drhePd/btGF9llxRqv6lMQnCOP3TJfDjp7s+SVhCSQ0zMhbXVmgW9rfVCW3TdDmms0BWSJup2Xuft5bfXD2lNaAfLVGdUeUGveh7Idz2ZnBeoUJzhiknZnPr6LGsgA4D2sfCymVgBFxJzihnzzzTcLVsd0WKD7S4OEK68Va8lMToNkj1vXZ2J/ly7tSYNkjzvXgD1nGiQYqLGo0n/iknF7toIVExmsLwOlQQI8AvKJLyY9EyD3xhtvNG7DEINZ6y37AaCkInrppZeMHgHWyWmEsLjCsozlFiKquXPnygMPPCCXX355LwvuqlWrDHDF2oMbM5MNNg2SHR/gHCstZSyhlj1m+wVAdrJAA7id/aIcLtToL5kFmraS+2/bHy3riUv2k9o31mnKlISbqU9fhL2aIzBugDAvdhZcJLZ4WVa820timt6ieO6MXvvc9sE3+zCJrnta0W3PTHEgLyKBvGZ1P9VJBWU1juvaqyDOq4DETh700pMq1jd1/1673Pah8oDKlC/KXH0t6ubcYqIEB9YKZDnT958+cKExftRXuVjk+bvVta/FjBQQPLmyVsZPrZfWphxpbw1KSMlwAoGoBHPCUlDcpvHCfV/4TDtjXFdDGR7P14988gS59Rv3KhFRRIiwTpbW5g457MQDkne7+rPX55XymRNk+5tbhq0H2phzeMZvt3S/uRLrsoQDaJ/8xVyZOLdJpu7XIOWVykMBEFYth9TiW7MhX7a9WSy71io/iz7TEZ7XpfvOMduZP4p9Zx8j4ae+p/n3egDwgHoJ5otv4QcHLJI5uPc1kAHAe1jnTmusbRpAhIUSy+H5559vdvNQBOw5BZdnyJmIZ01Og4T7M2Btzpw5BkABfHGX/vWvf90nDRJtA96I5cUNdyhpkIixxfUYgEoMMARSVmwapHT60l8aJJifsawyIcBkAOcjthlQSJogdGItrbgJ44aM4L6MXrB841qOAGhxQ/773/9uyKlgdAZ0Ll682BB3EbNLjC/nQ19MBGAFxr0ZN3TOY9Mg0R4WadJV0QYu68li44X5PmGBRrCUY0lm4oO8xghuzCwf//jHuyc8YKxm8gAvgdGeBmnasUtk7R8ekVBT4kWZMQPOPABhhb/pSPkBCzRGJhF/mU75sVjGW6m/fwyXKd6IcZ9Kj403JJ7Jve8jY1FXA40pryRPpu07Td5+MRFHP1DZ/o5VvLdCcgtz+zvsiv3+eUeoRaPvxQgQLhrXapbBFBH3BiWw6JTBirnu+MeuPVNefOINeempN3uNHU+YuLKxPbzhF1JYnHh+9Crg8g+kMKpev8uQWQ1HFdlqfX/PyTqx43LxZQVl0hGLZeujzyYmXBXY7lpTZJZBVaPX6KQjD1SDZ0/IyKB1xngBz/h9xDvlAImsf9pM/g863GCu+Pc/e9BimQJ7VwMDBffs3Z6M4bNZFmHyyg4m1j22srLSFAU0AWgXLVpkAOCZZ54pH/3oRw0IhNgJoIZFEtBmBVdqJwt0ummQLBEWaYbIicsapmYrQ+lLf2mQAJnE5B500EG2WbMGMFsma6zmiHNMfLbAFxCJwCaN5RfgSz+xsCMAXYA1ABrXcdyeibGFXAxrLYLLN8cqKirMZ/7QHvHJuJYDbJMF8iqE78Mp9juz++xxu2Y/FmW7OPfbOqNpPeHgfaVgyjhGNaxum1QfX750WHXHVCXfDnVffgdzkAqevbN1lr5905hSy3AGc+43z5WsIeYAtufJUgv7Od/sIbuz+9229hZNkIh3gtMhYcgqiGmeVv++xw253livwD3/109+XaZMLtdJwp7nB57k//vA/8jUygljXQXDGt9RajnPylVX0+GIqnnaogqZuSRjuUR9+33+EmUlHvorP9fufp+7eDjfwJitg06CZ/1Kwhqq5Hj1TjleHLyiZ9wnnmwluszIiNLA0H8NI6r7o6MzqVigAXd33HGHWQBvP/7xj411GOsk6YewhDrTIGE9tizQgGPIl7B84sqbnAYJrcACTfokXKG3bNkyaBokC36J+QUw4gLdXxqkofbF+S0R/wxzczLxFVZVXJkBmTbvL+NyphsC7CIAVwT3bqy46ImykGVhJQbcMkkACIaMCis2ZWgbyzbCd4JF15kGCXbqSZMmGct3KpBqWaBxk3b2ixRWTndpdMm5b7nllu40SLB1A5RZLJA2HdE/EKIB0FmSj9kyI20969j9Ha9xQ+tdrpJGjFs4a2iVxmLp1o3im6cvd9CXDkeUCdU7NU/i7duHU3tM1Zm9sEwWjm8Wv5I2DVX2n9ggMxf09fgYajtjoXx7Q45xux/uWNoaiyXW3DDc6mO63r///oo07GhU4iaNTZegWbJ1+7Yb7hvT434ng8tRr4yP/OASycobBghW4HHeLZe9k9OPqbpFc2ZoyEJAxzSUiWtlJ9cJiKLZ08eULvbEYJoaPHL7X0+SxuYc6dDQkGQJhbzS0haUPz9xhGxa0Zx8OPN5BGig77c2Ajo1GrtgrYDpsEADfrEG4r6cLFgev/vd7/ZKgwQxEq7MqVigcYNGIMOCJIp+WEtjMgu0TYNk+0o92xdctIlbRWBTxiILEReWZKymEEoBzofSF9NY0h9Ars3XC2BlQWw+YBiSsR5bSyxu0TbdENZuCLtYEAA6uiKmGBdjBOB61VVXdbsos8+ZBgndILhc4yLudEfmnFi999tvP2lsbDS6NIW7/tB3BLdsmwaJdo488khjvXayQGP1xyXapkHC9frUU081xGLJLNAA5aeeesq0Tf9tnLjZMUL/+NVvtyy3TWrahuK2F1cyoqiMy4+bmCJvwN23n3iHEokpiPUtUtKrZW1D+6ZVdf6D8nQSQgFfyOYiHFoTY6l0tLFOTt2vTnY3+2RLc74ObfBJBa7FGYUtcvLCeonW14mvIDNDH21tl+a6fCma2mwswXo7SksgI2+pyZWov0D4LnyFGcKcZMXVajqebAUTHW294wZrd2YmDJJ15fx84JmHSlNVo/zpv3/n3N3vNk94APOXn/6mjJ/lboLAZCV59YfqUwDcE7nf3w888Z5EWU+KsIjkdt34ub1O75GegPzm3sNlTkW17DNnh4wraVFjSFxaWrNlzfoJsnLdZAnH82RufU+4mBt1NVLH7O430D34rViLZjos0E5robMLtPHGG28YsAv4tGmQYGPujwWaFEjONEi0AcAFOBKnmswCjTu00xWbvgCC+2OBpi3A72mnnWaWdPvSHws0FttUDMnE8yKcjxy5xM/CXm0FoIwLNCRXzhy6AE3iebEAWzAPUHbqGNDb1tZmJgZsewBcXK2tezr7AbgLFy40LtS23PXXX2+s4eiVviP0EbDMOADjWIQ5B/sRO0Ys2Va/sFMD2BFbznzQP1jbmYBALr74YrMe6X+yJ4wXTVspOiUhu9tzdfxO8qvk3ifIsHIDISnO6hRPsFBJnTK3Hk/OZKMor7pBew7VNBPLFQSH9cVDwUS/4lNol+8T3/45mj5JX168uiMrY730lZTpi1pYLtl3rTy5ebK8XFVuWJ972Ml7NOpVKzGs0EsmVsv7p++UeCRPfKW49GfEm6/Muy1NUr+lSAonN5mXOdJ09ScAX377rbtzJNyWJZ4cTV1TXNZfcVfvnzZnUuI369ACEwwFpUOZRHRUdtHmMZ86UfLLCuTXl/9cyceVH6GfmZmoHiNW9UtPfk0mzkvcX12kpkGHGsjXSSpNZ4QOob2yabiSKwKLAb88YgL5meszWT98zp9QItGwZsHQ7AJrNkw0S6py2UVBKdCyGRl5Gsi8he7h7yTDAl1n3I77Y4FOlyEZwi1k5syZxp2YeqSaIn2RMz4X6y+WV9yQUzFK0wYM10s1LvjTn/60AbhYtbE4A25hggZUI6RHWrFihbEoP/jgg4aQjMkFUj/BhE0fkKGwQNMvXL5x32ZSAklmgZ4wYYKwIMQfjwYp2Geu+HJzJBhtkYl5LdKqaWdaIwGJmhy2CcCbmEP2aGxmRIqCnRLwJZBd/pyZo2GI//E+egrm6luGZrKMtosnzyf+Q/Ilti0kse3q4h9JaK+7E3zEWlypIKNcAbN9AVQE4smd0V3MrRv+4lLx5uRKrLVFjpmxQw6cWGNA8OraYmnSaxN16TufFGWFZJ/Selk8cbcUBBNcAt5CTaOUr7HUGZFgxVyJ7NqqbtBeadpeKFkFGpJS1GWxVJI7XowRQC9pkEItQelozFbGcp2IUfFm52Qs6UYTff8sPnqhHHHqe+WxPz5vDnoVXfg1fdcvnv5q38KZPX00cPBHDpO//PZZefvx16VArWwBnZjhtmivyQ6df27ST5d++0KZvGBan/qZHSLjDtxXtj/8LwNsIazknshTGT0i6JL5Lvt4Yd+4g/ZjlZEkDRRMLJHi6eVS9ebmpCO9P2KYqTjC3USVvTUycj4NMLc7cjo5mnoCC7RdYCPG7fXaa681rMDEzlrhBZY4XxbKY+3FEghYQmCBRpzMyxw7+OCDTb5cgNLll19urKI2NZGpoH9oG8sjLr5YJnGFxoJJvlnrAmzLsqYsLroIsau4KGOlxBW3PxbowfqCCzWxxM4FAEl8LItlSMaSivsyxFiMCZdkrL2kMUIvANT/+7//M67OsC1jdWUsiGWUxs2YvtJ36mMBtmCTmFoA7KGHHmos4tb9+rjjjjPxtrBDI2vXrpUXX3xRcBNfsmSJaQugSow1sblIMgs0fbYs0Oi1PxboVGM0DY7iP0WL9tVk78QT6fWjT80CBRYT81oNGC5X1+hxOe0yQT9Pzm+WMt224NenM9CTTj9pFI98z3XdV36k/lh7Jjw8GgsMwA0crkRs6t7se0+u+PZRQLFIGSQPy5fAknzxjg/0gF/tiidYqgA487LHt1JwzCkJVm3dLswKyzHTd8in3rtSvrzkNfncAW+Y9ZWLVspRavW14FcTs0oh9TJiNJB3xIk6GZPI+xvXyayOxhxpUGtw8658adudJ+2ao7q1NldaqvOkYXOxtNXmdYNfvBFylhyd0WQ/GmhvaJGStetksf6GJwXjMj07Lu/LDclbf3iqnxqZ3cka+OzPLpUmf1DWt4us0YikDeo087YubG8JK71YaZGc+rFjkqtlPndpYNaFp4tfn8FWALo+XfxdC9tO8OvXSe5ZF2XS91h9Ja9P/M7lybt6ffZpzPWRXzxLcksT99ReBzMf3nUNZADwXvgKrJut0/W4v9Na99jKykpTZCjMy7ZNAOJIZYG2liu7ps+AR7uwH0sv4izDZxuDa3WUDqM0s2+kNrriiitoolucYJWdjz32mAG4xxzT++HJ5AETE4glqErul+2PKaR/7HG7Zr8dH2vnfltntK29OtlQ8bELxBdMWHVt/306Mx9USy+MvH7ddj5MmWcOqBVz4snH2eKuXntyp4p33KGqg763YSzC3lK/eCcExFusFt9g3zLiU5A859Nj4nraExdC8QeWaM5ka8vo3SJMz6mE1F1Fx/IdZAQN5B11WgpruEeiIb+ENA9wR1O2Wn2zJNLB5Je+LTsEC3zxuZ9y7MlsWg2ENO73u7Mv00mDaimLdsp7CuIyL0fvk/qzfuJbd8vyDAi2qhpwPXFGudy35afKZmzYDySkz9OwLsqqIlPmTpR7Nv7ETOgP2IiLD5YfskjKD13ES8rgWtD3yNJF86X84L4ZMQav7I4SMw7dRy75241msHoZdgvbPHEOuOgD8r6rTu/en9kYWRpI8VY1sjo4FnqTYYHu+RbTYUjGsorASP3EE08YF+dHHnnEuDiznzaQdBilaQtQiyu1U2gbwcqMEMc7e/ZsA75xhcZaD2M0OYSt/KdYoG37o2099ZipUjyjQw0/qcFF3/FoOq/LNX41exiMnn0bGxN7/Pv8t7pdDN39FhdULL++KZnZeXsh+FpW6MsahEKONxF7MOU6LpM/0CC+loS3TcoiLtvp0Zfess9/RzxZiXvwUIZf+OHLJTBx6lCquKbsusdflWB+/3nPn/nJA67RxTsd6NIHlkmrEjN1KBGjBoyYpV0jWrdsq5GqbbXvtPkxX/+Q//2qxNRbwwnYkgetqak1JVpAjvjdzcmHMp+TNaATf9vag9IY9klH1GOW5ohXdsfUc2taJg49WV0j6XMGAO/hbwM2Zrvcfffd8u1vf9vkqMUtF3daK1gCYYFmsfGhW7duNS61P/3pT42bLyDMMi/jqnv88cd3x4iefPLJArFSRUWFaZJzOgUro3V9xhUaSyjuvdZ92FmWviSzQONODBBkjRszAHS4fXGeCysuFnH0gevyWWedZcaBqzhiWaBxPybPMemhSFV0xx13mNRPlLEs0LRlXZrZb8XJKG33Ja8Btrh3k18ZAQADeGGQhiCLmFxIt2CQxoUasRZoywJNv9AN7t64XiezQA80RtNg1x/OuWDBArNUVVU5D43o7cjGl2Sf4zfJuFnNSmqVIABL1WGvWoODeWFZfOF68TUs1/jC/sumqj+W93nzZ0vgvbcMaYj6U9YJfLW0H/ZnJdVRJrKMGA2E1jwruePbZeoJ1eqer9dYPxMzHt3vzYrKpKN2SzC3XUKreya5MqoUyVl0qGSdMrBrn1NPXI/xigOk5IKrnLsz2w4N1KzZppbzxMStY3f3ZtP2DHDrVsYgG689s1qnuLD6KkjTNQtTXj6/V1a9vH6Q2pnDXr9PqsJ50gRnhyqOBcDLYj8367GazsxEdTpXy4aXNygfSrY0RPyyqzNolrpwQNo0KH3lk2+m00SmzLukgZ4AtHepA2PltJXqsgx4czIXAzbLy8vlxBNPNC64ADoE1mKsl2effXb38AGoWBgBW6Q8SsUCDQAmZywut8QMA5STWaABxLRDe+S0TWaBBqzhOmyFvtBmMgv0gQceKCyQegGmiRGGCdqyQA/Wl4FYoAGWjBGXcPpKvmJIpxDGBsvz1VdfbeJ+cVUmJhdwSY5f+mtZoGFbpi2EuOHzzz9f+B6cbZmDXX/oE3HD1Mci/MUvfrH7MCzRuF6jr/POO8/sJ/4aAIyejz322G4WaOKJiVt+4YUXDCgntpmcxNYV2vZroDF2n1g3ANQHHXSQ2UWd0SDhzauk+Xfflzz1jtrnpO1SvzVXNr8wXtlglaRJwZnxjtQVLqkTNc3MlEX1EsiJSkTT1DTe+iUpvuoHo2GYe6WP7Vuy5bnvTZUln9opfgVmvkRodcpzhzu80lYXkHV/LJeDF9dLcFJeynJu29nx5svS8u+npXA+rKVRmXp8jTSuz5XWLcp6qjqDyRjGYp9eg/nT2qVwVquyxeqLs+5ree5p8bzndcmev7/b1JZyvDHlTdj4i9+rm2mBlBW3qkupThikmCrnhRlPhIamHOl8eatM2rhJciorUrbp9p1+TX9kyBJQWgrBpTcj6WmgdHyRIQ+LKAOvU8KdSrY4LhNr6dRJqu1Nf3tKYp1hadZc1ADdLP19+7ryp0fjXuk0RJbqYRQJy+YHn5YZp74/VTOZfV0ayI42iDfC5FbybzguuarljIxcDWQA8B76bmzcqrM5ABHpecivC8C0JEqAX6yY/8/emQDGVVX//8yeyZ403ReStpRSWorsWwGRTQQFcQeFHwiuqIA/f7+fCIo7IOKKoj8BRRBE9K/8EFApoNDSFsrS0tJC971Nmn0ymS3/87mTm7xMJskktDXJm9O+vDfv3XvfvWfevPe+95zzPXfffbezuCFRmj9/vsCg/KEPfciAOQpYkqvMOFaOYRmGGbmyspKPxpoKULRpkABmMCDDkEyeYEAdYM8KfQHgwpQMyMU6SyomCLMgllq1apWxDmOhRHLtS38s0BB8AT4B4ugIN2X6hliGZNyWcX9etmyZIadiMuH444+X9evXmxRElAV45tIW47311lu7LLnUxdoL4zNtIrS1efNmM1nBZ44BwtEJ3x8WYsogTFLAPm2ttUwUYB23fc+1X6Yx/QO4tpJ5Tdj9w2mNx0DjT74gyRZ9K+6851dMjUjF1I2S0OTv7c1+TQ+gaaMKE1JQmmbapf+4XCXqPdK++TFpP/UiCc1Nf+fDaWwHui+xHVtl69euFVG9/P07NTL77FqZdnSTAjMlxAoow7NPLR0JTVehrlVJJXlZ/bhep0tKpEaZjLd+5fNSc+eDyqPl7ts41+Oe278svrhf80ur3pRgyBdKSeWcFrMAcgHBvgLVZwaQQ7ex2qSpP/XORw/01z8sz7fuxq/qteeTSJtPtu0KSlFhuxSFlcU9kFAMB1+DRxLKEt3aFpKWiDJA67Ua8sdlzTXXyOEPPqDW9bzlyPnFMhn9u5v+JJVqXoMwMFO4L+5taJeVT66Uue/Is8Vm6ifz83mXnir3fLe3xxv5ld+24NDM4vnPDg1sf+YFWfT57yqDdkBinUC3PQWpKItTYNlOyeJrb5FgWbFMXHCU82B+u1MD7W+ukKq/Xi/eDuKkM3kRPDJ/7x+k6YFKKf3Q5/M6G4YacPeb0374Qn72s591tUruXqycv/jFLwwRE+7OgEoE5uWf/vSnZjsejwvWSYDqwoULzT6AHdZMywKNuy1WTkAjbMlYemGEvuOOOwwo/ehHP2rq2T+cE0vl1KlTjSs0+WVzYYGGsRirJwAV4AeDtXXdHkxfsDRjgXYKwJAFMAno/c1vftMjRRAWclyJEXTDWK/Rlyos6EuXLpWf//znZkKBWF0k17YA91avsD8zHtzOnWmQsNTj7o37NID5m9/8prGik8sXAYDbvkGYBTjGIs7EBu1giUbXSK79MoVH4J/2F/4uyT3bpKPdK8lmta6VK7DofLHzKymWf0ws66g61Hbe8WcAAEAASURBVFjfvj0gHZFmtR5/S0I35wFH7W/ulJQym1cVd0jtjhJ56cFJ8vJDE2Xswa0SLk+o63hSmXj90rQzJE3bQwZ8BBVsFIeiEtu5TZqf/6eUnnx6Vn27ZWfrs49LsqGuk5ip96gBvf5CRcF9SGyvpklq2yWti5+UohO6J6P6KD6qd0fWrZNG9WwRjbH06TOKtGaAXJbskk6NBDCO60Rs7WOPy7gL8nHpTl3d/t7bdMI7rpNZXhmjEzNOAfxy76xt98tPLvmR/Pdf/0eq31bjLJLfztBA9ezJ8uDK2+SDc6+TkooiMwcLOdYvnvmaBIL5V9oMdXV9TCWSsuz6H5rPRT6Nn071N1HlEcqkNC/90i//UM5/6m715soEyV1Nu3Ijvnmt7LnufAnr7/fqE5fLd58+3qR85F6Y1EnBD81fLZNKW6Xp9z8S39jJUvSO97lST8N50Bnz4cO5qyOjbza1EWtcewebBsmyEwN+EcsCfckllxhAesopp8i73vUuY6FkTZwpeWmZZbaCRdemQWL/hAkTTGwr1lxrtbRlM9cQTN13333GKswxS/zE9mD6AtDHTdu5AHAB2PQJCy9l+AyYpL9s03esqaRBwmKOezD1TjvtNJk3b54CgA6xoDSXtmBuBrByzssuu0xuvPFG427MOTlm0yBhgWbskGP94Ac/MLHF9AmrMEAeUIvrNH1h/YEPfMCAeNIgMRGB2HzCufTLVBihf6KLH5WO1kbT+8gafYh2X3p9jqhDvdUS9T5JNqUfosnt6yTZWNtneTccIBa66Z8aX66uZgXBhIwtbdaXOdxyPbJ7TbFsWlIubywcI1teLJPGbQUG/KKXmeNqzUtzqqVZGv76Jzeoqt8xtv7rCUm1qNVciUda1msu4HRURL917MHmNwqNdT3V0igAabcLADapEzIIbO76bqeiKC2rsN8jBWoZBsSllJNh9x//mLWkW3euW7ZO3lzypnluNWmM4O6oT2J6L7Txlq3qgbCpVScFVY+Rxoj8/sYH3aqqQY17xmFT5dnIb+XOp74qv176HfnNsu9IcWl3ep9BNeaSwtufWqpx6GnvPzI0lAfaO0fu/H1zJaaPUQaJN7fKjmeWdZbNr6wGGiEI63QpKtR74A2nPyeXHrlSPqzA97oFL8jMMel3JHVzlMZ7b5aOeHbDgG0vvz7wGsgD4AOg82xpkABkDz74YNdy7733yvXXX2+IlrBCAqCxDAPeANIQZO3YsUNOOukk4zILeHviiSeMNRM3a+JRrWCtdKZBYj+u0Ljq2jhVW9a5BgBChrVy5UpD7jRjxgxjpV2+fPmQ++Jsn21ieQG5gGnOAyC/5ZZbDAAG+CKbNm0ya1y5nSzQWHGpu3HjRnM8l7aIy7b5gXFRtjq3uX1xqUaYWGC8fAe4OONublwr1VWa2GKE/mEdRk933nmnGQt6t23ZyYJc+mUaHKF/Yqu6rzUAbdMLaesQ1oxsguU30ajg5OVw92GfX+Kvu/uhGl27yliFrFImVyj5WlmTukkyo9BTmcRV+7xJmTV+l4SD3W7lkVdfNNepbcON6+hr3ZwGzW8USbtadAcS3KKb14UVMHfHUEdXuPt6RGcN5EXXFzYEUAu4xaKRvh7tNZn+DDguCKhlk41OadP7KTHEeUlr4IX/t1TamtMTCuxpTapbeTQomyJB2azLnlhA5w87FahqXbdknbS3Ek+Yl4E0UBAOyqz51TJ15gT1istbJwfS19Z/PC/xxpauYgEFuJXBqBSqpTfgSZqF7Yqghjt0gl8Kx7QOdfPSrYGO9qi0r1Sd8CDplICGK00pa5HqiiYJ633TKR36Lh9b85JzV357GGgg7y9yAL6EbGmQAFdOwiNcgiFPwup43XXXGZdbrJOArrPPPtsQO+GCi7URF+hDDjnEWH5vvvlm+fWvf22Yp20cLUPCZfqZZ54x7te4QmNthQWa2GLAd6ZY8IsFFNZoXJgB7gBBmI6vuOKKIffFeS5ijrFaw7IMCzRCfDFxsAB6gL218BJ/CwkXMc5YV3FdxjIMwERyaQs3atpkcerbNKB/cCtHANZYhy+//HKjH74DhMmHCy64wGxbgEs/Hn74Yfntb39rrNYLFiwwExX0E8mlX5bIi/K4sePyjgD60c1wlo6Whh7dS6qLbuPisBQe0i7+Us3I6HxP1pLRjQFdeoKSjlhUUo3uZj5NNNT3YsQGBJcrM/Ge5mJpjob0WvSK35eU8nCbWohbJKiM2k7hQYwFWX3/nLtdtZ1qTk+c2UHXqeW8bE6zFE2L6gS9WjQc78Z4ImBhb9lYIE2v9yTMSTZ3ztjbhly4Tuj9xymAW3J6pzTuF7c+ftrANa/q1afA2Al+qUcapfjeeglNnMBH18uWlVsklez9vO1LMV5lMq7dUieT1c03L/1roEUt5uPHTTehSZu2vZYHwf2rS1q37OxVAu61IvNM6flcySyYrW5mGTd9TtTtSM8Q5jjojli7JHZsVN6T43KskS92IDSQB8D7SMs1NTUGIDpjgIkLhWwJpmZAn42ltczLzlO3qdsZy6WXXmrcgzmG+zB1AWmQLgHEINvCiksaJGJbsaBSxkp1dZoFGoBNGiTaA9ASU4srdDYWaCzImWmQKDdp0qReLNCD6Yvtk3NNKiEIuwCjgHtin0k7xLgAwDYNEm7EHKccx4466ijBSo440yDRFi7La9euNeARyznA37b17ne/W1ic8sgjj8j3v/9941ZNqicEfQG2yQt8ww03mH4A/ploADwDWG0aJNy3mUxwskAvWbKkC5g7x+hkusbqbfuFbq3wfVo2a7tvWK8z33q1s0lld25+oVA8AX0xLkka0JFq15dmjREWfXnOKlnayVputO7sY/xFoZgUhdKTKQMOnTb6aGfAuqOlQJbLq3FVibRsKJTwpKgUjI1pWiQlE9PrMbonKG3bC5QUy4GKO/XAJJjrJYsO2AXY9TmsQn3qKUv9Psu64IAnG+tVP+POX4P9KKfz0AtPrZQ7rn9A1unkAtLa2CZnVn1cTj7vSPnkNz4ok2vGdZbMr3poYJDXYo+6+d91D3WkPwzieYFVIK/DLDr89+7KA+B9pH9AGi7L999/f68Wcb39xCc+YeJHOYiFMJvguow1F9BMGiQruORCqIULMxZKBACHJZj8v4A2K7gHO1mgM9MgZWOBBlBmpkEitpWUR1iEAceWBTrXvgyUBon+Eu980003mThjm3oIMAjYxDqMa7R1P6Y8lmEswdZ6isWcsRKPi86sHHbYYWYzm7s38c2QaQFAnWmQHnroIeOSDdjFXdwKuZzRDZMNnA8hP3MmCzRWdXs+yjGZkcl0bdmebTl7jquvvtpuyqmnntq1PVw3PEWl0hFtzdq9DmXhTewd+Lbi8QfEW5pmLs/akAt2+svKlUis2z1yKEM2L9jqTu5m8RaXKaDtrcekshi3rCsySy76oR23izcwiJe6LMpK6fcQqKzIcsSduyYfOkVe/dur6nWA7XxgSWpqnzFTxgxc0KUlfv+Tx+VnNzwoLQ3pWNY5kn5etrRH5O8PLpLH73tW/rDmdjloVvcEs0tV1WvYRZOHPjFQNGV4e6X1Guz+3lFUKfGmZvXOyu1Esba4RNoD0h1wk1u9fKn9qwF3vzntB91iRbQCKCJuF0Zm0vDMnj07JxbovtIgwc5s5dprrzWbmWmQ7HHLAp2ZBsked66ZdXamQcIdF2BK/4mJxQXbClZMZKC+9JcGidhfrKSAeNidAbVYexGbSghXcPpFXC7bsDQTi4yFGHCOAIiZTMBNGastbt64m1t2bduWKax/sCYTA4zAmm0Jx2gDUIzrMZMBZ5xxhimDmzMWYM5H7C9EWAikY04WaFynAfz2fJSDQRv9ZTJdU9+WY3skSqB6trTjAvQWpCOmL8qHHP0WWhj5VX1hJb/pzGM91NF4RWONUuq+5mIQXHDo26T1X48NVYVd9QrmujvVR4deRwWeOmnvcnTuUk3OGwF/TDqadDJy7JSc64zmgsdccIw8c/fT0tqQfcIwc+w1R0+XguI0p0LmMbd/fuKB5+TWq+/uUw3JRNrV/MOH/6fc//ItAlt0Xro1MPntx8r6P/wj/bzo3j3wlhpmJp1+7MDlXFRi6XfulcLWIhlT0pw1tVmmKjAA/+v7j8sFZ787z6adqZx/4+c8CdY+Vj7WWLvgtjtYFuiKivTsuY0JtamHcH/mGDHAuOBi/SUuF9dZC0rtUACOgEZcn60rNNblXNIgQRhVVqYWFbXEAigtGKTtwfQFF2pih50LwJCFGF5YrZ977rmu1EFYcHF7Js6ZMWEJxxpKXlzSR333u981ccLEKldXV5uh4jqNYOVG57BGE+OMuznCOBCsuldddZUBvzaVkbXmchwAjPWeNUCWHMos6O711183KZNwNbd5nBmDkwX64osvNmUtoKaPLJzTMl1/7nOfM6zRfG+2D5x7JIq/VG8bubhD9jM4r1/JdYqK+ykx+g9FFLT5/by05WYd6q2RDvGHOpRc45Xeh1y0p2jBOepe9hYHrPfMopO1HRdL/M1XpKi4zcRND0UNHiVvKy6NStuSvw6l+qisM+PYmVJ9VI3GUOf2G/+AuvDmpbcGIi1Rue1z9/Q+kGVPIpaQ2z6fW9ks1UftLq96HOV6HTqVkFLvBV9pOj2lc79bt9tqG2TdX56RtVvH6XtybhDqje3jpXVXg2xZuMytahuW487t2xuWXR85ncrGAt1X7617LDHFCOAWQHvEEUcYd9+LLrpILrvsMgMSScmTLQ1SJgt0rmmQLBEWLMwQO7EGgFoZTF8Afs4USGzTX4i6AKT0m7axigM0sWQD7hkr/QdswobtFDspABM2QhooBDdoK1hdAca0wzbywAMPyOrVq+X4448XZ4y2rYNr+R81fQeTBldeeaUhHMP6y+cPfvCD5jPjod8IbTvFfmd2nz1u1+xnjHZx7rd1RtLa27hCvJoiZcii8YShcX5JvvnskJsYDRUjzz0mQWXRHZp06PWpLybJiLQte2poTYySWv7xE/Q3+dYeZbiSBya4222y/aUnJSAtUhCO6ZWRG2DrvoT0WtR7QlFhs7RrmrS8dGvgbZcu0Osz/Qzo3tt7KzKxRKYfNb33gfweWfz4yxJrz+1eyVzDq4uUE2RP2qssr760Bjb/82VpTajX0SB/2q3JgGx+5qW8Gjs1sP1fL5utlmiBvPDmQWa7L50mkh55fet42VZXYdi0Nz6+KK/HYaSBvAv0AfgysrFAA87uuecec3aAES7AECmxxu03Mw0ScaeZLNCQOUGshbUUQqb+WKCtKzSsyNkAmAW/lgX6uOOOM2AQF15cjMnBa1MyDbYvThXjToxbMaAVd2bOi9UV+cxnPmPW9O/MM880LssAfyzDxCOTCgpgbicUsK5bQivckHGTJj0RQJU4YKytMDMTxwugBuhaazn6og8zZ840BFuU4zuB6Zn46xUrVpi+QK6FyzKg3LJA796925yHyQis4pBzAZatMCYs6ZBzAeLp11133WWs6oBl+mfjmKkDaRdjQ3C1HvbSWisFY6IS2aV5F/siuOpnEP5wQi2XyhbdvKufUqP/UKpxrwGxoVBcr4mADrjnxEr/GtD8qwXtQoaaxM70tdN/+dF7NFVfK77CgCTUSjQ4HXbrxFcYlORevR6nz+ne6bKt5K7Nxj2yalyTbN9SKckEAW65XpMeGT+5Xq9nJcRr2O0yzfU/3L/89hlZHo3IQf6glHeGKvC0AIewJPT5vznRLpHNbdJY3yxlFT3Zyftv3R1HX9N8yq1NveP8+xq9P+iXNS9tkOPPmt9XEdftr31tvcQVkLV2+KVYPbAGEkBda1LTFSr7e+1rGwYq7prjDevVY7A5/Z7WFCmUxa9Pl9lTdkhxWINHVGfcMcmmnFDr8Bvbx8nO+m7ref3qja7R00gYaB4A76NvyVoBnRbGvligAVqAXlx7MwWAiLsvgMumQcIVmPjVbCzQsDwjkGEBgOmHtTRmskDbNEi2r9SzfcnGAg0QJJ8w4BISL8D5YPpC+9mE8X3ta1/rSoMEUP3GN75hgKItj3V38+bNBkTa+GCAJDHJTsGtGyZnm1IJsAoYZTwIYJ14YxZ0aIVJCRYYonFVX7VqlTkE4GUfrt/ERb/00ksmTRFu1rSBvPe97+1Kg8T5yCEMoLbpmSgHSMcl2vaLlE4AdoA8wN/JAg1Af+qpp0zb2SYnzIHh9EfT7viCKQmPVevj7qLce6Zozasuv+EqBSpJJRSLunuG3qQwUu0FAljTAcFpkrX+FZpOPRNWK52dc0m1pEMB+q83eo92tGruZEUUXs3DmNIXvMGJXpNqufT6vJJqcff1aK8jPAsmT6uTLRvGqirxXAGu9SVp/Y2fWN/pzq81FOzlJa2BR+//p/zz0RfNh02JmGzTe2eJXqwBneQl8UybvgtEHLlETxzzMVnadL+6oofzKnRooLG22fFp4E1STw0GMA/c4sgvEWtK/y5jKb80K1lloV9zeOuwnOTQADj9b5aIWovjHUyCaRhZU/rdx3xw+Z/2+p7PiaZIWJauna4pCuMGBJM3Par5vVs0jWHmBGK8NfdJHJer+YAMPw+A95GaAaxILizQTmuh8/S08eqrrxqgBvi0aZAATX2xQJMCyZkGiTYAuMSwEqeayQKNOzSpeazQF0BwXyzQtAX4Pf/8882Sa1/6YoHGMkoMLWAUCywAHoBLzLBTsJCSLsoZqwtBFazQWHkZB4KbtrXM8pkyMEpjRUcAs/T9nHPOyWpdtQRfWGERLLqQcznl97//vWARt/mJ0QlWaZieOR8WYSYd7MSCZYGmvNUv7Ny2T7acPQcEXFZGAgu0srQoXouIvyAlheNbpa02bHKr9m0N1keqPmkDhXEJVabdyEWtIZ7SiXbYrlx7SzTWvpMFGhDs9ZKvOqC/Rwvi7Br18FqiXFeaEzgU0vhpBybxjXc34ZCvSu8FHQnVDTrSnLVdcVlO/Rn1Of6k9Qn4NbfuVLv4qtx9PfrGdV9HuOxOrd4jTY2F0tRQlH4pNtclOtVJmE4OgKKiqJRXtioHW3dIhNvZ3e1Ftvgfr8iXLr7dfjTrhOquHtK6PsSriv/0ed+Suxd+3Tw7+ijmut3h0OBeVdvUWuzXSa28dGugaHxl1weAbWOciZiUBLxJk+aMg0n16IqnNJe3mfTqvn8WTeiu29WISzeKJ43TNI9e6cjI7x3TCYO9zXhy9S3hqm5rcN+l8kcOlAYGd1c5UL0awefJs0DvNXl++2KBJpYWsRZZQLCdPHB+7bAo//jHPzZu0QBYADqTArgqA1aJzcXCTqqkqVOnGldjSKywtAJYcaE++OCDTZMwceNa/L73vc+4QgOgaRM3aeoikHJRxylMDODGzOQDABfgjgyGBRr3aMjLcIFmUgIZ6SzQnqqZ6r6cdnP0qStz0cRWibWoC2qrgreEvnTgl4vgHq3//QrYguUx8RlLZ+ch/e68E2anP7j0b2DaLEnW7uwaPQCusDCmEynqPqV6TKX0IauqxCLHAmEWa6d4CsISmjnXuct12/4x+lKhLqRcbIBZr5eUZB6dlOlbFQA49A3QQzri7eKvcvdLXmD6PGkLaViDppRBCKsuq4hISVlE2qNqDYr59br0qd5S4g8oY7R6IaQnHUzxrj+Bg9zrRm6VkEgk5caP/9R+zHkN4dDq5evl8d8/J+d+aEHO9UZzwcbdjfLiA88LcLafn3QPFfC8fvJHT8iC9xyt94M8EEY5wbJM0kkFuwqE48m08aaHAjM+hEoz62YUcNHH8ccdJoGisMQGaRX3aM6kqacf4yJNDf+h5u8M+/g7sgzQrPMs0L1ZoFE3ccikIYIFGmKsTMGqCnt0TU1ND7Zl3LGRZcvSTHqPP/64sWZjgQVIA5hJ38TDz2kVBzwj73//++W0004z28QW42ZtWbdZY1Vmn12wTFtrLbG8eRZoozpJ+icoOEtv85cX5VBpXIFwRIomtUrhuDYpHNtmgHHx5BZ1lY72AL/U6YgpoUlVeoKCz26UwgXnSsqHm1RPAVSEQkllDY/r7yOungc6gRBM9gK/1FJoLAVv6+k90bM1F3zauVC86o1gBVDr9+uEgU64YJlEn7hHs+Yz+zluwS/1/IVaf9vfbROuXIcOP1UvqG49WiWAH8J6HZaWt0llVYsBxUXFajFXffYS9fApOO6dvXa7bQcAtrlxaK7grc1t8qOv3GeeY27TW7bxPv6Tv4kvllSCts7ZqmyFMvaV6kNp68qtsnZR+tmfcdh1H3knWv/kS2ZCdbCDZxJ2/cKX89djp+LGHjFLLea975MD6VWntKXm/Pyk1kB6OpDH8wD4AGjbkjY5XY/7Oq0FXIA/ZDDMy7ZNLKqARYicHnroIQPsIJgizpV0Pn2JJcLanyzQnPuxxx4zRFiwQGcyPXOcGNlsunISR1GOON2JEycaF26sulh0qYfl1qYkohwAGJdl4m6tftnvFFifv/CFL8i6devM7g0bNpgYbYjHaKu6utqQV3EwM043s0173K6pwwPILs79HBtpElmn1t8+yK8M0ND4YCzD3gyQYcfJO3a0tUzi69NEY3a/29aFp5wnsUTuL3WZ+mESoqV0hvhd7gKdWvV7Ka5pVre0ni8lAFzAmwW/rPnsBL7olHrF1c2SWv1Qpopd9dlXNUl8ZTDvZwG2OWrCk0pI6Fh3p5NCVX9/eLE055j7N5tqG+qaZfO6bu+QbGXcsm/xQ0skHo3LeK8vJwiMW2OFAuCITkA8//ASt6ip33E2btwpMZ1YUQeDQYFgwC91oo0t0ri573fHfk8+yg569f16UkXLIEFwh1QURtVrZmBr+yhT17AeTh4AH4Cvpz8WaJigAYI//OEPTXwrVseBWKABxzApWxbonTt3GhIm51AuvPBCw978i1/8wpBJwQINC3ImWLN1LPiFBRrrK5ZU+gEL9PLly02e3Gws0Ln0xZ7DrgGa5CgGvGYTy4QMGH3wwQcFF+b/+7//6yINIzYYQXccg6WavkGkBWEX+X9xsWZMiAXAt956axcpFczZgFwr6Acyru9973vGQk1bAG7A+OWXX25y+NpYY8sCDekV5Ff9sUCvXLnSTDrcfPPNRvfo36ZTsucmBzKEXyy4dQ93Se7eJm11IY21HHxPzQNV3XtjLSFJ7tw8+AZGUQ2vui8v2qYTK4Ztd/ADS+nr4Ip2d7s/o7WO5i0aWx6XgHohdLnfD0KdwfK4BCvi0tHk7usRlQUclvRBqLCrqL9IXc+jLV2f3bqx9tWNb3HoHlm/eutbbGPkV0/EE1K3da8ZiF9nrg7y+tUSrJNaWYbGviK9J07VMkwydyhyW5O3ABtN1a7eZCbgUxrbq3g2J+FZTVnqILWrNpm12//E62qlPBiRsUW53ef0SpSgcndMG98urStecbv6htX4s91HhlUHR0pnLLCEBdouuOQCyAC45L0lZRFimZdhgWax8aGkwoEFmnpYcWEwBhBDjAQj9FlnnWVA2+uvvy7nnnuuQKxUrZZJBBZohH5YSyMs0LQD2OScsEDzmeNWbF8yWaAhqyLmlvKMAXfjwfbFniNzjTXWiu2r/czasi1DPPXTn/7UuEEDIGGBxiKLizRCnxDijdEjIBjASh5gdG6BNACYSQLOC4MzgiUcqzjlESYUvv3tbxsm52uuucaAZmJ/IQCDTAyx/bIs0EwyMFEAgRf9ymSB5juHBZrYY8A2LNCIPaf5oH9uuOEGE39MDHLmMVtmOK07om0GwMaac2Et7u45l52+l0jTtmLlLIpLst7dM8rxaExW146V1/dUSqKL+KpbX/1tMSv/+xWHyg41xrte4mlmzbLDmtSay72t+/7Wr240Vtirrualc5rSxdo71/1WGt0HPfEWQ1aXsw671KHu5UFNb6bsxR1Ne7r2unWjoS63l+O+9APwq93Znd++r3KjfX/99gYJhoG8aYEkbIoC3LFqDS5WsEsASYEuZbo9QfeN1zRTTg+rxt353zSaa91dL8m4ThCqJJVbAv4mA3Cz3Crtfp4xyc7nUiqWMG2YBlz+J648NPpiLBNKW2RyaYOyaDMV3dP7KK0i9TjSY2XhNjl0nBKlqv5NXZfrbzgNPw+A99G3AVBEYIG2C2RMpLeBDRggZeNd+yJloA3LAk1blgWabScL9JVXXmnIm7BYWhZoyKAQ2gAEQ7iEhRUWaKyQ9IUYVxbnA8KyFAMSIfDCrZi69BvwSVuWBdrG1ebSF1ig169f32OJd96ATUf1D21j8XT2h2OW+Zm+AnqtAMopSz0E0A9JlY3NZZ91k4bUqrKykl0mJRKg9R//+IfJmcw+rMjNzc0mRzCfkRNOOMG4jKN3dEt/YXq2YvvF+S2I74sFmjqWBZptJivs+G3/2Y9cffXV8stf/tIsAOnhLp5QOm61bW9YIp2WYMecStbu466bUtbJhk06+YH7tL7EeMvGZC3rlp2BAp1AUMUsXF8jy7ZOzMkSzItLW9wn/2/VLNnVUiyFZbz+uVz86euRWPSqE+okVKX5kY07dJa3O6Mqjf/V46GqmIw5Zq/eUzr1FxhESq/RqvKQ5jzX0IVgES/L6K8vHVoFpMv4dCIhUKCTr7GoeErc/btGMyVlvbktrMZyWfuUMKdybFkuRUd1mbJxpRJrSwM3O1DeAYr0xz5Owe5kX0Am6TJGtwu4AWRISWWevAmVFJQWiice69IOVl0mXQG5FvDaNfvSx9Bn581RU3jRRl7US0b5YvQl1KhibHFEZiu4HVvUqlbeuAG8gF6YtSvDEZk5pk6qKxrMM8aj75R+6uZl2GggzwK9j7+KPAt07izQpBvC4mknD+xXkSvbsmWBxg0b12UmGACSAF3SNV1xxRUGyBJn/Mc//rEHozRWV85L3l+nYOVdtGiRAeHWgmyP59ovysFijXU9FxZoYpetWABvPw/HdUeh3sSbG0zX2hsLJBH1S7gyqmzPeB/oI9O+h3Q+XAG80YaQtGMx7owdTibUYjQmuwv8cBzz/uhThz5Ei7xRUS5deX7LFNneVCInV2+R8oJ28yANOEiG0m7SSmRSXyGLN0+WxmiBmXUujbrbim6+l8LxGqSWvh4Bs2VzmiXe7JfIFmXqrO/0UuA9Tq9H3ueCFTEpnBqRQHGGD3+RplNyuSRiXr0a03HTweKYkmv7zcRVt1qsItN7sLgHSMuloBlJtcfEU5R/yZs2c6Js2zh09wyfplmZXDMurWQX/w2Ggxp+5FMQPBQldMjEmvQk+FBqj6Y6vu2r9dbXeQPsGhgAOA2C07syj3cV1MgSneDaxbvSKd07XboVqFKjTLJ7MiGok6mTyprN0p9KOvQiDh88q78i+WMHWAP2VfUAn3b0ni7PAv2o+XJxCyaW2LlYADkQC7S16FIe9+NwOGxcwy+++GLjvm0JrnBzhvQKC+rMmTMNyRVuzcwQA2SJ2WX9zW9+U6ZPn96jLcAxllhAqlOefvppE6OL+3KmpT7XfhF7zHLVVVcJaZ+wFn/uc58z48CyPBKsvE6dZG7vaa3CcNklSX1JbtlRrCQZJRLRnMBtdQUSrQ+pdTgsLTuLzP72JrXSdYJfKqZwByqr7mrDjRv1//ibTCtt1Xi2tDI3N5bJ/a/MlfteOUz+tXGavLhtgqzeXSnPb54kT647SO5ePl8eXzvDgF/0FVAX3jF1rwkxSW6W1o65ej0CzLolUJIwQLjqxDpj5a2Y3yBjjt1rLMRlhzb3Ar8pTZvUKvO6G3DhVqK+Tlq3Rrpi+yEMC4aV1KokJgFd+5SJ3BdIr/3KTB5SgBxSS7EFv0x+tevETOuSZ12ovZ5DPueDJ0lhydC9M0IK/GbNO6hnoy78lNRsAeN8LWayb7DDD2iqs8rWPJEYE60dT/9enzMZE369FNrzHuo8jFUz+eT9GlftePA7C7hoO9XaIIVq8RXVyWDE79N7Z2T7YKrky+5nDeQB8H5WMM3nWaDfbty5rQv4QCzQliQq0zU603UYF2bEWQ5ATFwx4BXWZwiryA+cWRf3ZARw7RRcvxFIsJztsi/Xftl6dk1dG+vM2rmfYyNJUvowfXNlvIsYw9n3Do0tircGBbAbbShQ1smgAI6N2c1REDfeXQ2Vsv25Vx173bdZ//RCmVW0U/wZuX2x7r6yc7z8U0Hw42/MlMVqHV5TW6Wuz92xcJgzy0PtUhVul6ZlS92nPMeIt71E/unsL29YhL1BTYlUpGmkAj1THzmaUNDnke2vOPXrPOqO7dblS6QtUqT3qp66RIfk8MbFORBOuzr7lem9y9OjUz0dOgnRsisgzf/6hzsU1s8oz/3wAg3H6fQ+6KdctkOBkF8u/9KFvSZgs5Ud7ftql6+SWeqx4R/0m2qHhNUyV7BlrXookSPcvRLbtF462lplRpV62w0StKE1nycpM9SVtyPSIrHN692ryM6Rx158XEqr2kxGgcEoo7yqUaJP3jOYKvmy+1kDg76t7Of+jMrm8yzQPb/WgVigc2VbhrmZuFzYnSEJw90Y4i/kwx/+sBAzbJmgIaEiNhvyK+KsYYFGsNBagcQK6zRxx9XV1XZ31zrXfnFOzk18ci4s0F0nGAEbrdv2aKxqSHbsLZOEgoahSDLpk/XbKmTP8teHUn3U1ImsXiUhfUk7omqP+Af9YuKRY8fvklSkVZkl3T2RUPdardRuKZVkHyB4oAuGens2lkvdCne7k7etfFlSrW3StKe4h4fHQPqzx9uaQ5KIqeu5tuN2KSwqkIuvPVt9OwbH6t+hNbxqaX/fx890uwrN+HcvXSGB9haZW96iFkxcdHMVvT8q4PCHAlK/4o1cK43Kcu3r16rlNilTypqkKIjr7mD0qHwA/qRMLW9Ut60OaV/XM2RsVCpsgEHFlj8u/o5WGTs5N5I6jz7by6uapVBzp8dXLxqg9fzhA6mBPADex9qGmMkuv/vd7wyzcCYLNKfEEjgUFmhcaJG+WKDNQf2DlbEvFmhbxq7pSzYW6K9+9asGyOHGnI0FOte+2PPYtSWQsp8z19nYlulDJtsyaZq++93vmjji66+/Xkj5hFX47LPPNu7HtGvbOuaYY+TnP/+5YWTGZRqQW6GEBDBGI+jgW9/6lpl1P+KII8y+zD+2LScLdLZ+UQ6rv5MFGhKsvligYdmGAZrFMklnnnu4fI7sqlPLj1fWbJkgrVFNhTQ4LyAzjOVvHiTtas1s2epuwJFoTMetzlKSjEMq0qk+cvmeAcvnHrRBLcDpOKTYzh25VBu1ZRLNLfLGoqnSrt4Hg70eKQ/p0xvPT5F4Y9Oo1VEuA4vv3GaKRZsLpKUud8IbfbeWmPIANO1Os/snG3J7McylTyO5zNyTp0mkIHcXXMAvS8GcOgkXpondRvL490XfI9s1jlqfzQcVR2VuRdrjq792TcoZDQ05fWKdFAc0xEk9lqK17r4e43XKQNweFZ96Gh07bYuqz5OTSznMxiF/Qk6ZvkE5KRT/KsFdvNbdz2yuvWR9+jddEI7LpJo9+vxI6vt2thchPBFTUjG2WcrGqMu0Skd7RDqSg5sUMxXzf/aLBvIkWPtIrTU1NQaI3XbbbV0tQrIE0DrnnHPk4x//eBcLNAzEuOW+//3v7ypLLCoWRgicfvKTn5gURJYFGkInmJdJg0QKJNx5OUa6JMsCbRvCckk7tGdZoOkTLNAf+tCHDIjEymmFvtCmZYF++OGHDQs0OYNZIPUCTB966KFy/vnnG3KpXPoCC/Re6OIdMnXqVAOoHbtMu5nuyZZtGVZmYmdxa4bICp2Rd9dZHmD729/+Vp555hkDHhk7ExDoHfZq2xaxxKQ5WrZsmYkXRjdYkG1bWIfXrFkj99xzj2GVBsQCihkr27hM27Zsv+gL6ZOIVXa2RTkIuvge6Tt9IlcxuYwRe06rh7lz53axV6P/4Sw+detDLx3KIrlszXRZMG+N+DpS4ncQNmXrv1ZRi7FPVmyYJM2RtNs5bblZYIW08raxtVISiMvyPeMkqS6oyc7ci/Y4a5glCzSO6ORJ26VSibKsePR6c7Xo/SmpuZRffGSWLLhkhf6+PEpwN7CVA8tvtCVo6qWSXkOY52Y9ekLpsBB00FpfpODBK6VjW/X+r67jWabK+U3j9tymIQ9Yja04r2u7z41rnhkthZulJdokY+VgBR0+tWJmUaQqJynKiSBtslmWyilFJ7pRXVnH7A93X5PTS6IG1K6sL5aI/t4NgZNqVa9C9aBJ/97HFsQUKLdIkd8CEo+mOnP3/dEb0PET0K/vhAH1ODpr1lpZvm2SNLSFle2Z6zHTk0v1qcCtXNP3HDFphwG/5svRNryOe0TWL8wFOz2B7smpgHprTKqpldamAonoxGFCMzRwX/Tp9YfFt7gsYiZYu9VCBoLu5373/vzWv0MD+W9iH2kdF9tMAeiQfxZGYACmJVECyJE+B8uwUyB3mj9/vslrC1gFVCOPPpomlgJEZwquvwBNm/IHS2N9fb0BsYA+gNnTTz9t2JFPPPFEefPNN7vy49IWfQHg3n777QaMYrnERRhrKO7Bq1atMtbhOXPmmFPn2hdy85IP2Sn33XdfD5dj9MO5AMtOsWRZ5B1mEgHgjIUawInVubi4+2WLXMmwSe/Z0517El38+c9/NizQti0mFmCKxgUaAdzTPm2hgzvvvNMAO4i2nPLAAw8IC+7MgGakv7Y4zjlfeeUV+eIXv2hcqhknscboH3H2n882zzDb5DIezlIwRlNzYDZTIVbwn68eItMn7pFp4+rMvkDXi4f5aIqSd7A5UiCvb5moVuPuF5qiye5mOfWXlUtSr0ErM9XNbFpJs6xTMqxNzSXSqlbyeMqnbtKaUqGgTQ7SYxxnNt4poQnuZtMOlKn7c6RN81z65F/3zpNDTt4slZNVT3ot8t6XKVy+qYRX6raWytrnpup2OoUd7bhZAhMm6buwXly8wam0NYXVqh6SwoqIhJXwCkuHiQ9WsAHwxeLesrdQ9d7zNcJXUelmNXaNvaamWidCk9IkG6RZdikInqm5a8d3QjZ0jCXOoyzwrVIn67XcTr1ePUr4OK+rDbdvFE2doC7hykSueWiRcQVxte7WS1PMJ7XtQWnT3zGWTeJ9xyn4DWc8fzzKph0e625Wcn6P3oKwpFrSFnT0dfSUbdKgz+KtDWVS21okcZ2cRgL6rKlSgidcnssKNKWZ41kD+PWVu1uX6Mg3vkYSa7t5N7yqz5LyNrNwvD/x5lPE9aeeA36s55PrgJ9+9J3QCfqwHmIBxDWXtDu4PNuYU0iayLOLkB8WEAhQXbhwodkHgAIAA1ixTF544YXGvRfrLSCwurpajj32WLnjjjsMQP7oRz9q6tk/nBNLJeARV+iPfexjBtRivcsU+oKFF8E9+Utf+pIBbwD3L3/5y/LOd77THBtMX3BXtoDRVNY/FozyGatqX2mQ7ESBBbWkMAL8IlhWGROCriCrOvzww41+0S3x1lavWF7JJYwALD/72c8aKzaTEV/5yle62gIAZ9OLqdj5BwIv26/+2qI444R8C9D7m9/8xrha33XXXWZSgnZGMgt0sYLWnmySHlm/Y5xs3j1Gxmhi+HHlTVIQjOtLSUqiCuAaW8JSp+l9mjqtvlanPIQnHZeeVLH73LYuPeEk2bMFl7Tu3yQpFQ6trDdLLvrwhAul9AR3W4wC1TXStn2XeVnDErzq6RrNjxyVibPqZGx1g5I3JQxg8+g1F2sLyJ4N5bLzzUqJaAovK9wWQ8ok72YpPuYk2fvQffpA6vYuwArcUlusC5pRlz4FGOxzMro7dcaVXHLy6c5drt3mOTB79iGydOkyte5GZLukY/UDEha/aPiIxgfHpV3X3XlueTZcdNGFrtVZ5sAnnnK0rPzRvV0A2B4vVctbaXDg3EiE61TOm2WruXJdeMSx6nbbkwHao8C2Ihw1S85K0TYK5x+Tc/HRWjB45NkCEVZHJB0+l/M4vT6hbl6GjwayzI8Pn86NxJ7gzmoX3F6J+wREQoyEtdIKVldbjtRJuDTfdNNNYlP8OK2/lL3kkkuMJfmUU06Rd73rXbJixQqzJs70L3/5i3F5drYNqP3Od75j9gMCSQ+ENddaQG3ZzDX9xFLLORFApBWsv7n2BTDKmJyLZYEeKA0SuXCPOuoo49a8adMmA/JtrlzGhdszgtsygJiYXHQIQMVybs8DAKctPrN2plRi8gCZPHmysQbjnpy5MFYs0ExqzJo1K6e2aJNJBFzQTz/9dDPhwWfAN31n2+qWsiNRKgojarfoBm2MAffmXfVl6uI81bhGP796pryssb4bdo7rBX7TY+6QYocb70jUw1vtc+WZZxk3vrfSTiqekOL5b3srTYz4ug2xkHEbdw4EcLtu2WR5/iFNKXXvfF3PMeslf5gj61+c1AP8Ug+38wa1KLlZwvOONOnJ+taBRgViLc9giXaWx0IcmnOkc5ert7/znW/2mvDE1blNGhT6tvQAv+T+ZTL3xBNPcLXOnIMvmzlNiqcNzcPFVxCUGR9MT94723Tbtr9ijISm1bzlYQcPmiG05XYJvu0szXWunnCDFE8oLOFzPjHIWvni+1MDeQC8P7Xb2Xa2NEgApAcffLBrgcEYIicso8S7Ao6xDGNtBEhvUUvRjh075KSTTpJ3vOMdJiXPE088YayzuFlbCymnBLR98pOfNCD5oYceMr3AFRq338z4084umhXgF1djmIshd4JkCgvm8uXLh9wXZ/t2e6A0SJQDyK5evdrEAC9YsECIVUYuuuiiLtdwmJ6xhqPHl156ybiZ33zzzcbFG7DJgptzJBIxExC4OUMyha4WLVpk2gPg00ZZWVmPBbdyrMK4pPNdoNNc2qJRzgHIZcLAskDfcsstXf0xJx6hf2K7dsqEMs1dqxbeoQo5BcerK2/01e5Y9KG2NZLrxf3Favshpnpoo1BSTmmKF2tcVndM0tBaGtm1Nr62S9p1AqY/PcJO3JdQry3plw2v7uiriCv2t23dIa2pUhO2MJQBo8dYKiQt29PhEENpY7TVOf30t6s31TUDDovnS1Lzwz366J/Nc2LACi4qcOT1n5RAaXfYU65D9xcVyuzL35tr8VFdbtynviSecNGQx0jdcZ/64pDrj6aKHrXkFl9+q75op41EuY4teOYHxDd2Wq7F8+UOgAb6fis4ACd3yymypUECXEGSZAV3WciTAG3XXXedAVzE9wK6YDXGOol7FJZLABvkS1h+AXyk9IH4ycaY0iYu0xBDWVdoLLL/9V//ZYAl4DtTLPh9+eWXjeUYF2aAO3HHgOErrrhiyH3JPBdpkLCE9ifHH3+8GSukVcTcYsFFLr/88q5qTAQwWUBsL2RZCHrkZWLevHlGT9aCjRs3BFMQZnFuQDWTC5lEXbZx2KURG/vMdq5t4VKNZX78+PHyqU99iqqmHfrLpAX5hO14OAaAx80d4fum3nCVRP1eJTITjRFqkM31FVnzAfff9zTBBgA4vsvdgCOm7KQNiUqp8u3QZ+ngUDBgI6GuqE3RgHFv8+g171Zpa2iVzdESObi0oYsMZzC6wNllS0uxTlq1DKbaqCsbq9srbakSCXY0S1Bzf3Y6AeU8Tso3aCqkitqe5Ic5NzBKC15//f+YsJ0vfOFaaahvUt+Zns/fkuISmX3oIcr98Zcu76VRqoohDWvccYfL3Ksvlpdv/pV0qMdXTqKx1Oc8coeEKgdvqcup/RFWqPCIY8Rz8JGSeOXZQT9rGKrnkKPz7s+O79w3e7Z4Lz1aUnctc+ztYzOoz+Zjp0jwvHP6KJDf/e/SQB4A7yPN47IMeHHGAOOeSxwrTM0AIhtLa5mXnadua1O3KF0uvfRS4zrLMdyHqUtqH0DeBRdcIJBtYcUlDRLEVbg0U8ZKdXWaBRqATewv7eEKTVwsrtAA20wWaCzImWmQKDdp0qReLNCD6YvtU+baCX6JPc5mlQaIv/jiiybGl+PEH2Mlh1nTKbhDQ1JFzC36Q0/ELgOyESzqCO7IgFFiq4877jgDlJcsWZI17RBWW9zFsTAzkWDF2RaTCViSLQu0sy3KQcSVyQKNRRgAXFdXZ3Rr28XSbS34xI0PZ+mIaXygvumOKW6TmFrNtithU65CWgVifw+bpGQv+rKcGuZjzXVcQy2XbI8p+6tfdjaWyuSKRmPBzAV0MH8FsdgO1b2vUNlQ1Q2aSR/XiprCo3otrmsqk0N0YmYwghV9TWOFtKf8ElTCIjeL+T3qBVjXVCwT9HokyiGX65HJGBh59zRq6IeuU3pd56WnBj72sUs0zd075Nx5H5R1dWuU+ErT0uhvv9xfJddc81m55qufdvdvuKe6en2afcVF6go9SRZfd4vEmtUDSZ8f2USnbaRoXIWc+suvS+GEqmxF3LvvzI9K24vLlCG7J7lVfwpJKtlda6JAKs/6WH/F3HesIy7eeZNFPq2pth5cLRLROP62jMmZkD6T/V7xnFEt3lMPVh11x/q7T2HDc8R5ALyPvheAKS7LxKVmCqy/pOSxsalYCLMJL7FYcwHN//M//9NVBAsowAgXZmsBhUAKSzCWX+JfrWzc2JMF+tOf/rQ40yBlY4GGkTozDRKxSKRfAogCjq0lNNe+5JIGCWCbjQWa/nzzm980s+aQVTkF6ynjdgruxrgwE0NNbDTWXdy3EZu6iDRQWJMRQDTWcizh2cD3I488YtI1AawDau688sorTT3bFrHcmYzSzrYoBxjPZIHGAoxknvNrX/ua2c+fU089tWt7OG74x4zVt920BWNiWbNhjdxaX64vvoYip88u4/Yc1jQ/MzXdj315CcI662IJVVVoSgSvYXreqvHT49QqTvoJdQLJKgANljYlF6tTi6XCEyUl8rs+zUeotFDibe0SSQZkdUOFzChpVCsHky1Z1Wh2qrepJDTV1IbmMgOe2VlQ0b9XSt+tjY4jwaoxnYDXIzv1eiwvihhCO7BGX0CYW0FCmcr3NmvaJNWntyAkwbH5OMFsVwScFF/+8n/Lr77yeyVji2tqKWVSSHTI59TF19UTWNmUlWXflDNPkDP/cLvc+84vSlmyOR3y0AmEzb1R74eemYfIuX/4hoQq3M3onkV9OjFQKU81zJGawDqZVKIZNnQyuq/fNfoE/G5vKpUNyRly/tjybE26dp/XW6nzgzrdMlMZtv9HSShfr5PUa3tE9kS4IYqUF4hn7ljxzKkST4G+r3oC+lzP3xeH2wXTE0kMt96NwP6QN9cKoIi4XdyQb731VmWEnJ0TCzQphLKlQYKd2cq1115rNjPTINnj1vU5Mw2SPe5cAyCdaZCwkgIm6T9xybhgW7FpkAbqy0BpkLCS9sUCjeW0sbHRWHXf9773CQRYAH36hXv4NddcYyyvtk+UZb91I65Ra7wVyzwN+KUeOZmXLl1qvhNAemZKIvr15JNPGuDLhIZTbFu4njsZpW+88UbD4m3bohxWaPSXyQJNe7acs+2Rsh2c2BO0VhVHpFTJrHao9Ye8gvrcdJDkaLoUfSkpUOA7ScFyqSOtgkcnIYrmzh8pw94v/SyfM9NYb2k8qSACi26hMmgXh6ISCuhsMoCXlzr9yxrg29RWoCkrum/b5YdpftG+3mL2S6+HX6PjDquWll31pmNYglerRXeM6rBKU0fZ/KDOXpP7ck97geyNamoQ1auVCYdPt5uuXBdNr1ZAZq3gSgqm6VH80WT6egwm9Doj/3f6ekRB7Qm/RKIhs2YyBvHqhGHZ4YeZ7fyfnhrgedCwXVnJ9fea1NkZr65nHj5VGnY1yrhpeWtlT21l/1Q+q1pO+tGX5fsX3SZleo8MKWt+Uu+TMcJBEgG586VvKfgdfLxw9rONrr0T3nawhsukZHX9ONmi6Y9mVdVqrt9oD88jgC+Pk3p9zrxRWyUtSjAYKlPOjiOwYObFasDjJdRuquafX2smskSBrk+XvoS7oz9wSF+H8/v/TRrofpP6N3VgtJ3WaY21YwMQYUXFcviRj3zE7OalFXIlp+DyDJkTeXwBewA5m3oI92dieg8++GADoAC+uDeTWglQ6kyDRNvEEuP6DJgcTBokLJ5YU7FCEwN8xhlndHVxMH3pLw0SLtjf/va3DTkVVnGsuk5Zv369Ia1i3x/+8Aez2OMAS0AleYoRCKcAtqSbqq6uls2bN9uiZm2ttrhzwxaNnHbaaUbHAHDc1J1CKir6k60tmwaJ79Pm7sVSTu5gJj4sgzfx1CxXXXVV14QHMcp8T4DqkZwGibQSxUcdK41P/13Vln7pDWp+0IPGNMi0jgYD0ohNTao1iJyCgF+/zjRnikctdMXHnJC521WffaGgjD3+CNnx5GJFuujII5FY0CxAX2bosZzj7oxrqdW3VZK/uFCmvqf792n3u209+z0nydbnV0kskr6PYIncEy00S9CbFBYs6wDfmE40sGRKsLhADjnP3dejN+CXiuOPlj0L/4lPs1ER7O4NkSJ18ePq0+tR9ci1aPIBd/7+nbr06f28+GB3TyQ49eHcvvbEm+TNFzfqSzOBD0iHrH95k1x60Bfkh8u+LrOOzuvNqa++to9815Fyw8Ib5XsX3ibNbTHUKJNmT5Sv/+FaKa7Mg9++9OYLBmT2sZWycmGzAbbLt09WsJuSkmBMeIYjMWV5b9ZnUIfeQxGeP7OPGyM+vTfkpacGCos+IM1N39frT2+OA0go/E7Vdc/wvQGq5A8fAA2kr/IDcCI3nyIbC3Rf+rDusTU1NaYIoAlAe8QRRxjQBgvyZZddZoAb4DFbGiTcqZws0LmmQbJEWKQewo2Y9d13393V1cH0pb80SAOxQGONJvcvbss2NZEF+IBMAD1Cf6+++mpjfeUzoDjTGoYFHmH23SkW+GZaeZ966ilTjPzCmW1ZoJ65335ntn173K7ZT0y2XZz7bZ2RtB777hPU0tO7x3qZGgtmabhdKgrb1HIUywp+RS1JxTM05nLCxN6NuGzPnGv+Q/zB9ERCz6GrlUgBG9ZeAF0m+KVsIOyXqeef3rOaCz8d9t4FCszSgC1z+IDdlkRQGmIFZp0N/FJHQ7Vk9rvVlc3lMv3qK8Wv99hsghcCngrpl+Pe16xPWXenf/4qdevvPcGQrT037fvb3c/ImiXrDPjNNu5vv/9HGhrT8xmVrVx+X1oDdbubZcvuiNS2JmRve0pefnGLsvLmX2cHuj5mBJebSWkza6CF+S03qTdMrXp7sLBtwS9lePYcHHhpoGZdeRyLbmHRfwww9oCECs6QcOF7BiiXP/zv0ED+jnEAtJ6NBRpAds8995gFkPnDH/7QWIch0iJ+NTMNEtZjywINOIZ8iVhViLUy0yAxJMibSJ+EKzRW0YHSIFnwS8wvrM+4QPeVBmmwfclUMQRVWKeJicomgNyxY8ea47gLA2KJFUYAwpZEC/0RT03M8AknnCCLFy82QBcCK5iVGZMlzUJHzjRId911l2nPSTqFNRnrNOfGApwplgUawi1nW5BzYXG3wnmxpP/gBz/oSoMEWzdAmcUCaVueuGUsz9b6bPcP13WoIi4l02P6oju0FzaPr0PGHjPwrOlwHf++7FfZIdNl2oyIsawNtt35760WrMhuFywbs2YVmbjfoerikDkleSuHKq94Ro1Mu/RDOiMwOIsP4Lho5nSZeH6e6TTbNfj079TLox+JNEdl00oFcXkZUAPR1qh8/cIfmHLMayfiGsig8dS3XPKzAeu6uUAqopwd0Xo5c/4aVYOGMqh1ty/B8luoE9gXHveK+No0vrUtTSbaV3m37g8VHK+Eqp+TvVs6pLWhQ9ojHRrfr9v1HRJRPsZYy2lSWHypW9Uz7Mc9uKfcsB/Ov6+D1gqYCws04BdrIO7LmYJLLSl4sOLaNEgQI+HKnI0FGjdoxKZBoh/W0pjJAm3TINm+Us/2JRsLNERcWJKJcYXEC3A+mL7QfjaxAJZjtq/ZyrHvuecbTviRAABAAElEQVSeM3HI9vj5559vN+Xxxx8321hzcQ9nQQCxLGeeeaZxQeZ8EGfhTk0aJEA16ZMAziwI/cCCDJDFzRxXa/Y5xbJA40ptUyrR1imnnGJYnAHQCOWw+gPkbRok2K7PO+88QyyWyQINQ7e1PGMdxno+rKV9r0w4uUGidcqeu1dNwUqWkbOo9bf6PXskUFCYc5XRXnDqpK0S1/jfrdvH6DXXPZGSfdyaRkonHuYdtlGK/XmiF6ujMeGYjCuIyM62IrVb5H494tY7uahFKsN5khery4mnHyNv3vFLddTgJdnu7XsNWQ6v0tNPn9XLa6bvWu46sneHvg33I6i5XmOB8zKwBlob26SoLCysrXSoy/7OjUpClJc+NZBqVp4Ef1DdnVvlgmNfkVc2TpYt+gznHkioA+LXsCXun1PH1Mv86u0axqS/bH9AUk114n0LeYT77NQoOBAIHSk3nxmQcTOV62S2OiLoK1H9VpFdbxbKx39xpIyvHgWDHKVDyAPgffTFWhbHXFigndZC5+lpA0snYBfwadMgwcbcFws0KZCcaZBoA4AL2CNONZMFGndo4mWt0BdAcF8s0LQF+AV4suTal1xZoAGv2VyCOe83lQka1mkrAEoswFhfGQfx1LBmsxCXiyWV8ZID+I477rDV5DOf+YyZVACsEtMMqEYHkydP7srH+9BDD5nUR4DlRYsWmYUGKHvJJZeYtEk2npi+UQ6mZwA0FmHAMvsRywKN9dnqF3Zuy0xty5nC+gfgTVsI5GfDXsLjldQwJDUX7JGtf6+UyPaQkjkNANzURdUXTMnkM/YqSYnGXYfygIPvOb77YdVlQqZX75aiwqis3zRBryN1NXUQXaWvBwW+GqdVqEDv4BnbpbioXVLe13Vmfr2+mEwf9pfM/u5gaGyVTC1ZJ0F9gdvaWqIx6ECK/tCbXo86GQP4nVAYkdD4cfu7iyOi/diWDbL18x+SYCgs2+vKZIwhriMWvXf3DfO76rkpHtSXvoQ0/uV3Epo1T4oXnNW7sMv3TJk1US28+lbch+D+PHF6/hrsQz09dpeMKRZ/SFGGdANgLMAVE/LPlB6KyvjgLdUJ1ni72Rv0p+SYmVtk3kE7NH1ZsbTF0KdIWEkYxylhZShgyfDUOKCpD33lmv0hL1k1EK9vkKKQR7a8ovnkdemWVin1Rbs/5reGnQbyAHgffyV5Fui9JgfuW2GB5ivBurtw4UIT74uFFXIwXLOxrmLpPemkk+TnP/+5YXWG/ZmJA9zIESzVAFcIyYiTfte73iUQWAHeAckWbAL8AbK4UWO55zNu2TBP4wYNUde6deuEVEzEBAOykcGwQOMeXVFRIbhcMymBAMSdYsm12GcnUpzHh9u2p/wQxRbpW8eUM/dK07qw7F5Wqnl9vcoiq/PJXRZhfXEOqhVd/5eom2/V29QFqyjtduWpmDPchnXA+9OR1FzKW+8Q75SgJFe1yfhxTTK2qlnq9pbInjomWEI6M++VoL6MlJZEZExls5SXRdL91Lhh7wSPxLb8WApm3X7A+z7cThgtqjK8TeM19rxQwdhWTRPVqqywXHuaWKqruzrdZ3BxkT8uU4tbpFhJ2mCRbS/Kp6hASXV3KamLTuRV6qTAazvGa3xlWCpCbVIKUY5OLoixCqvFSOPTm5Qsp16P84s+vGKHpFpbpPaOb0vRCW/X20P6hbpL8S7fOOOyBfLyk6+p1bLz9+vUh04ulCqoG1+TB8BOtfS1/YNP3y0tnYR38OMjHXoRrlj6pix6ZLmceP6RfVV19341TSbbk5p/ulsK9F45tap/74RkTLXs8fU7ndjdoru2Evo++tL7L5G3l0fl4b1KKqbXY/qK9MiJY3fJhs9+VsofuFtK5hzqLsWMkNHmAfA+/qLyLNBpRuq3wgKN9Rhwe8wxxxjwylcECCaF0Z///GcTV4uLMQRWxEGzOAXyLty/YYuuqakxh44//ngDbq+44gqTVgkX5127dsnMmTMNALakWABeFqeQNomyMEkjbmaBZvyeisMkoQ9Ff6dVqHRGm8YEtxl36NZtCtpalQVawXCwJCmhyrgUTtQZ5FD6sUD9eLtfQlO7XdnZ50ZJ7FUX/lRU0ycUSvJNnSlWnULmNLYKINzUv0p8CoCnKnBufkFS7ds1F3D62uy/0ug9uvhf2+VQBWVBddkrUVB7aEW9SY3SoGk8oureF9MFAFegS3mw3ZSz2sCFd9Ez2+QYu8Ol6/iu7dK2fBFxMZqzW/UYapfGqBLkRJUgRxeE2MA0KZtVUocU+BMytjgdI0isYMuzf5eS0861BfJr1cDxCsoOPX6GvPDEit760Fvjrc98RSc/uydqehfK70ED37vqf+Uf9y+SeDSdotBqTIPKjIKuf/dt8u1HrpMTzsuD4MwrpvHJx3VSNayxve36nMk8mv2z3gqkTYmxGhc+IRXn5J/ZTi2lNFvIopPP1Ptlh5Qrr8nF09fLyvpyzS/vkRq9H47XFFPIiqs+L0fc+0sprDnIWT2/PQw0kOPPYBj0dAR3Ic8C/Xbjzo01FhmIBRpLLCA4FAr1+NZXrlxpPgNqjz766C6GaNzFcTsGNCNz5swxx4ilxTUZt2hSHmFBJi4YwiwEa+uJJ55o2oKw6otf/GJXm7BP474MGRbxw4BfS16V6bKd6dJsj9s156IfdnHu59hIE4/XJ2tfPkSBbPftg1jBgjFxGXN4i4w/oUkmndogVUc2S0m1AjwH+GWs8Xaf1LfMHWnD3uf9Tdb9TR+eSoA1PijeyUpm1a3OAc/lP6FYrWyq9I6EJBsUtLhYGnbUy9ZtEdmrYLcze4/RBmB4XLhNpqmld2ZZo1nzmf1WKE9O4M3rm6SlboBJB1tplK4jLz6reUK7XR+PPciSMqXBBcPOBL9edSNfMGODSdlljrc0Seuzel3npYcGXn3qNXlFwa/T+kYB5hCxQnz3Az8yHkjsy0t2DWxes13+fu+zXeCXUvySu3/N6XpYiPOM2mldOP/uffTP0qZETSn1KsqgN3EW69q2Zaiz969/7tqf30hrYM8TT4pPw9ysMgv0uXJ01V45fmxdF/ilJFbiTXfelVfbMNTAIF65hmHvR0iX8izQPb+ogVigKysr5ayzzjKxzb/73e9M/mRIurDMAqIhoQKUkq8YKzAx059VV5PDDjvMnAhgyzHWgE3ifrEck9uYuGHinREIqqZMmWLaOuqoowxTNvXsQpktWzRORmOKAeP7iwUawA+IZgEkD3ehj6ueDElTbQHGokHL0v+bIbWvrh90vdFWIRXt1kHg7AqNi9bbMW/E/YkW8R1ZJP5DO1PVqAU5FXm9vxqj/tiO1VvFp/eD5XVjNMdv7o80fmrEtr5UV6Xx6X7ZvmrrqNdVfwNsX6vEf9HuuEryUJ89e61MKGnWNFHqOqmLvu0ZKzCfyzQ++BQFv4Yox9Fw+zp3X48OVZjNJQtXyPXv/p6xUXJ14hxuF8AvP/nVi96UO67/nT7Peual10N56dTAQz96XGKdlt/+lFKrhGMvLkxPlvdXzk3HeGa3vcHv0iMNdTp5qhddf68aHEupZ0zdrjJTJ7r29RHxbnIgv9M9f3tSkq1ZQhoyO6HvdfXP9c8Cn1kl//nAaCD3t4UD058RfxbYmO0CeCOOlDRH5OslZZEVbkiwQLPY+FDAFizQP/7xjw14I92QZV6GERpQCPBDzj33XIFYqbq62nzmnE4B+OEGDAgk5RAgCxZoPmcKfclkgSaNz1e/+lWTzgfLKTG5Q+1L5vmIux1IsMZCukVsLhbe559/3oBf4nxJB4XQbwiksPiS9qkvmTp1qikLwAQAE0+M9FcHl2h0xvL5z3/elM9kgaY+usHdmz5lskDznQOyiSnmu4IFGoEF2ilYqBkDC4Raw13aG5r1ZS4gT/1mtnGlSnUbjfrtekLdohf9sUa2rymWyK6eOui34ig92JFo7hqZR12aQ/8xTryzw8qipm8nmT/TgO4r8Ih/QakETujJ/twR297Vjhs3GrfvlaQCh6gSh/1j+2SjAqclOJtOSLmKa/SjW6YqaPapVSQpjWpJdrMkdu/oNXxelI+cul1Onr5R5ozfLbPG7ZFDdX189WY5sWaTyfudWSnZ2H9MYWb50fx54Z+el2svvFniLe39zm0ROXj/bY/IyeUflWhbmqhoNOtlsGNb+vQK+dMvugkx+6ufSCTlk2d9XWp35a9DqyczsaX3OIRMA3t2lEl7m7I7632QBcDLYj9zrH4Pzxm9AVBH34c62qNmO/8nrYHaF3KfZOGdqb3B3R5Gw/G6SaOp4dizEdanmpoaA2xuu+22rp4DNiFTInb14x//uAFwHMTqCPHS+9///q6yAC0sjLAe98UCDQB+/fXXjaUQhmiAciYLNICYdmgPQqdMFmjAGimCrNAX2sxkgcbFmAVSL8A0aXycLNAD9SUXFujm5mahv9lAeTgcNqAc8AhgxR0ZkA+ox9UZFmiYm9euXWsmEMh1THuUc8Zhw9JMDt4PfvCDQsoorMBPPvmkYY6GDAtmaeo4hTy+gH/GDfi17M2WBRoX6s997nMGlJOPmZhg8ixbV2jKAaD5HmGb5vsgJzNu1YgtZ88JKCe9FIKr9nCXYGmRJFVHqbhfHvzG0XLqxWukclJEggXZkXA8pqzGcZ8s+bNem2sqVa8dEixNu8MP97Hu1/55Q/rG0W1x43oLnl4uqaN18mWTsjzviClK07eTYp/4poXEq4sn2PNaNf3zMUvvXgmXF+prmr69qQBmH90yReZX7jXuz+rsp9bLbt0kOlP27NJYuFf2YjFOzzR49H5ZWObua9Jb0nNipVtrYoBuYbDRuavPbW/Y3Xq0inn2seVyzXtvNR+LREMcBhCsv0kFJ1edcZPc9cw3dLI7cxZsgAZG6eEXn10l//H2G6VIJ139+k+Tcw04UiYUPnD0F+Xhl74vFVV9X9cDNjRKCvCb7Ig5AaxHmhv13ac5JYFQXL079Dmjak3qpCAcHakMT5qOeFS8BTo5mxejgb9dfbskm/T9MUcExRX78Hu/Ih949Fbxh/W5n5dhoYEcv75h0ddh3YkNGzb06h9AZ+fOnSanLADTsv0CfgFmWIadAhibP3++wKBMOhxANfLoo4+aNSA6U7AM792bZl7mGJbG+vr6rjRIALOnn35afvnLX5p41zfffFMikW63DfrCi/ftt99uQC7xyjAqY1klhnbVqlUGiGKdRHLtS38s0Fg5b7nlFgPE0RHnh/H59NNPN+fgD+AUa689HymF6ANu0E4W6COOOEIuv/xyY522lf/2t78ZcqvjjjvOgFAst3/84x/lgQcesEXMGis6VnFrmWVnY2OjGS96QtCtFcsCzSTF9773PUOMxTEmCrCOW3Znyr3yyismppjJBsYIoCfeGLHlzAf9A8GXFRvHbD8Px7UnqSRC/naJKgBOJryyUC3B4w5qllnH7pIJM5oMwMV9yqvEEK31IU3/USlvLB2v7mvp2w3pfMJta4fj0A5onzy+cp1Z722l8Jb6xTtPdTWv0825n14xY+8Ppa2e/RQb1YfKSH9iXu7SSLc95ZelteP05SQu4wvapFxJXyC/wuJb3x6SXdGwRGCIdggvh6UuT6MSmDDFoZGhb/rK84zaDXXNcsNlP+5SYqskFQJ79V928MbeGJM4ar18Y8Um+et9/5R3X/r2rvpu3cAa/p8fTk8KxzXal2mu7BrsqSHK1qkF+OufulNuf+g/ex504afkjrVqaEiYrALO4QN02zXbwEBC3eSON8Q38eCBio7642888pys++tiGaMTMmFfLGuKuEwltCV80rhph6y493F521XvyTyc//xv0kAeAO9jxeOya4XcvVgAf/GLX8gNN9xg3J0BlQhWx5/+9KdmmzhWLKYAVYAgAoACAAPESPODuy0WTgAVbtDV1dVy7LHHmny3gETy4zqFc2KpxP0Xq+nHPvYxA2pxG84U+oKFF8E9+Utf+pIBbzAlf/nLX+5y3R5MX/pjgQY8YomFpRlgvmzZMvna175m3MTnzp1rJgfQF2O95pprjAUdJmby/9JXQLllgaZupjDpcOONN5r2mZhgsqEvwc3cAmBcmDkfEwjEAVuXZlvXTmAAsIk5xiLOxAZpkrD4omsEAAzIB/QyRmcaJGKYrQu3bXekrWPP/komTmqUDes0bhXmJmU93L2x1Cyi1t1AMCl+zfkbbQ04UiJ1j5JLsGLnfUq489/i0dQMbpUWzfkbHrtRJwqGroFUVFMhNR6krNpDb2Ok16xofENfinUmgGvRIaRBWt+i11ffP/+u0spbLmWN6/RzTdc+t214x05RgIF9rfczIlddmJrl+XQ+v/vxXyWibs9WWvT6KjH2S67S3hBul3SXjTRH5XvX3SNnvO8EKSxSkh0Xy5/uelJaOlNHxVSHQGC015cVGMtvVNRdV8sk4klZ/I9X5M3XNsvMw9LvXW5VZfQPN0m4uE2a92LF7XmfHFAnyvweLo5I9OGvS9Fn7x2w+GguwPvz4u/8RhI6MVPvDUtVWEks9Z2nP+F9Z09bkWbOiMnS7z8gB593khRPquqvSv7YAdLAIH8JB6hXI/g0ADi74PYKuAJE4lYL2LKC1dOWw2UXl+abbrrJuORSxmn9pewll1xiLMlYC8lrSz5b1sSZkpcWl2crlAco2thf3IU/85nPGOBIOp/+hH7ed999xipLOUv8xDZAO9e+APQZk3MB/JGfd/HixSY2ljLEGgMIGYfNk4vVFfDLfgivqHfaaacZCyrjxGUaqytuzQiu4LgXW+ZmxovFGECKSzH6R/73f//XlLGTBRBjAZQRxn311VebNVZlXNfRoVMgwqIvrD/wgQ8YgEvMNhMRyOTJaUsckwj08/TTTzdWdD4DmGmPbXQ4kiWx/GGZNX2zTsR0X3Nd41EwjAtVW3MwK/j16MN0yuQ6ZeJVF9/NL3VVc+PG9kfrNYXRWxu5phKW2mf7/02/tTMM/9pNT/9N0x7Vic+THFJn/XpNHla5R5qe+fuQ6o+WSlH1BsoyPzq44enLXvuO3YOrMwpLP/bAsxLtzFVrh7dDQa7e9QyIU7pDhXOaT1k/AX6N9dcW1HVKk1O/8PRrjj3u3HxELeGtzd1hIg0Kb9NTNL1BB+AYnbZIvEtZfAcL/7K067MbNzri7ZJYu1hzTbeIp+crTU7q4HWltLJVEmueU48lDctxsdSt3iRte9PcHYTPbG8t7veemQa/hdIYS09kJWMJWf/EEhdrcHgNfQg/h+E1gJHQm2xpkABIDz74YNdy7733yvXXXy+465aWlhpwjGUYayNAGoKsHTt2yEknnSTveMc7TEqeJ554wlhnsXhCEmUFgPjJT37SgGTiZBFcoQGNmfGntg5rQCBkWKQbgtyJ2FcsmMuXLzdsy0Ppi7N9tgG2WEbJ8esULMZ2DLghI1jF0RHjBtwS74xQHys4KY0AsABWy9wMuASk2nHiVg6IB3gSr4v7sSUSQ8+URe655x4Tl03cNBMV6JzZPqzN9AHd4OaM+zjbWKOxENPnRYsWmTbsZAH76QcTBuiSSQdcvgHAtDGSpUMZr1K731A9tktN9W4dUxYQ3M8AIeA4bM5W9fdTp8CNva33/VQdVYdIN9Pw0m5pXOnTnMlDG1pC3wu3POyXvc8vH1oDo6RW26pXZb6SM4Uc6Y0GMzTyA8+rqpO2Fe6ekGldvkytZr5+X+j60ysve0lNsdKuz6oOx4Rsf3VG4zHcn2v7IFTbrVB3m4K43Qp6Ab7bdFE2hV5qaG5olSVPvtprv5t28Axf++rGHkNGU3skolpLW4MBvWngm9K9cdmr2nRKXAHHPx990bnLddvJravU00qjpzW3d/nYZn036X299aeU8nHKAq91Pf6A0JabZdvilZJs735g17cXyvomzf2rIV/kk7cCCSOfazXcZkekxO5W7pSYbPibuydkupQxDDbyLtAH4EvIlgYJcAVJkhVAHeRJgKTrrrvOWDmJ7wUwnX322QYAYhHF2giAA8xh+YXg6de//rUhiLIxprSJyzSxstYVGmsr8a7EFjutxfb8Fvy+/PLLxnIMIAW4E3cMGL7iiiuG3Bd7DtbEHOMSnEl8hdsw4J9cu1hvEdyycRO3emLMgGFcwRE+szgFvW7atMmwaVugi5syAPjWW28VdGoBNvHSM2fONNVhuUYsQzRWcATAzgLYttZz2Lwffvhh+e1vf2vaXbBggZmosPHCjBGL9vjx442lm3aIX2bigkkLxogV2Qqxzva8uF9Tb7hKR6vGRHvTt415czerLotk955cSJg69DtPyVlnvqLfr87Qq7Eu1bh9uA5zv/crXt+oavTLht8EZG51SrxV6to3iOnIlKqwaZVX6pb4pbC6O059v3d8GJ4g2dwkYY0rf2fNRvnjGzP1lbj7RSSX7r734DekQOsnW0b25FQuY+2vTLKhXl/afOJVimx+q4NxVAH8Uj6e0Pj1kDqka9iJTycY3SgA4P4IrIAfmRbfbHraucXdTPlN9a2a3qx3fAi23ybVIKKJDvX3rs8S89fs6vVn7x53/647Wmp1UisNekvHRDRcyyfNdfBL9H+f9OjkdplajUsrI0anTGp1NNf20q+bdrTu0ntke7eHAWNviYfk9foqKQm2S6HyTkC8SEaCJrX6WpJFp45ad9c7P+a3/40ayAPgfaR8XJYBq84YYOJCiXXFcgkgsmmQLPOy89SQPrHAVIzrLIL7MHUBfMTNXnDBBUJMKzOjEDhBXAUos5ZR6lRXp1mgAYLE/tIertAASVyDs7FAY0HOTINEuUmTJvVigR5MX+hPpgBysdZaAeyvX7/ejIt9pAgCrANYKQtRGMRXkIP96U9/MvHQmTG96JlYayzG5PMFTNrURbQJAKZdJhguvvhiYxGGyAomZyzvxPbS9rXXXmuAOQzQ6BXwT/krr7ySZkw/WfP9MJmA9RcADnhfsmRJV8ww/SaXcSYLNBZhADB9QbdWrGs1n0eEezRvu6avIgtOXi0rVk6V9RvGG+sPFt5MCQQSEi6IyfHHr5XCcPfsqQYPZxZ11Wd+o8k2j6z8ekiO/onagTQFaOfcQr96SOj7SPMbXnnjju5JlH4rjPqD6etofFFEPjR7jTy4ZpYZcSrLtWhV4dVX5pCC3nOnb5BSZUE10nld2zJuW3M9IoBYj0df5DQPcC4gmGodGvoQbdd4a16qXa5Hxm91aRQ6xD/9w5MhNjrCqnVgSutHcHkeUNx+PaIgh5oqxzdLMJSQ+l0l5nfbkcH4DPD16G+/YpwSe5Z3W9TfCjfAgN/RiCngUKSjz7C3N7SHzeLY3cdm/pfdh2IO+O48AN5HKgeY4rJ8//3392oRt9tPfOITXe62WAizCVZRrLmAZnLfWgGsQaiFCzOMxwixrVg4SQ3kTPuDVdPJAp2ZBikbCzSAEpBIyiMsm8TiEtsKKMQiDDi2LNC59qWvNEg2RRBjAMjjwozr8Ic//GF2mX0AQvLn4jZ82WWXmf38wTJMnK3Teoq7MrpAZ1YAy06ACRgGfNoYY9o5+OCDjYs4uZqJz8VVnHRQ7IfcyrpQo08r9B3hnJks0FjVbR3KMZlBLmOsx+zHwo8FGLHlzAf9g7u6FZsOyX4ebmtPUaU+THvGWs6bu0WmT9+lEwTjdSKi0rwI80IM23NlRYtOaNTKpIn15oXajqfDo5am8n3DOmvbHEnrQEWZqjGtR0DwC58tkOn/EZPSQ9UarLg2GzEWrtIditV2/N0v2x8BbKQlWKXfiYvFV1ImCQ1LQCoK2uXiQ1+XZTvHy/oGZYdWiZkXPF46NP2WF517ZEZ5gxwzYacUB3XWoVN8pd0Tc3afm9aByiqJ79imQ1ZitXhQ/Mr8ym8YyQaELa7A7RnQbMAvhfUd0avPPLdK5bgyvccPzs20l670ch0/1d1EOaUVRftkIqHK5ezunuIqdYHW0AbHRVZcril8StXo0hqSSFOBZnNIhz74lcCysCQq4SLNW505l80zu3SsoxX3bRZNGCO+gqBxZR7q6IsmuPt5PVS97Y96eQC8j7UKiLQCKMJlFzdk3G9nz55trJscx9W5LxbovtIgwc5sBWslkpkGyR63rs+ZaZDscecaq6MzDRIM1BBy0X/iknHBtgKQRAbqS19pkLCWEheLlRTgCrszoNbGxtoUQeiHfhGHzDakVMQiYz0FnCOAU6y16BWAiSUbkPnYY48Z6yvAHYEpGmttJqM0EwjkEWZCgvRDuGZjSf7CF75gYoXRAfplUgPrO31HcD13skAD4gH8tu+Ug0Eb/WWyQFPflmN7pIlHkVmiYIr4Y2t7dL2oMCYAYZZcJKHpk3w1x+VSdFSW4YWkqKZamle9bsYHCMaiG56SkrEnJqTiSM3PWJJ+ZWG+ob3WI3XLfLLnX35JtHTPIHv0Gq480b16RHmFhx8lTU/+1eiRPyXBuJw+baucOGmHbG0uVle0oDTrUhKMSVkoJpOLW4zLc1eFzo3C+Udn7nLV5+JjT5DI6lfVnzR93SXUjS+hqaN8GiONS7ST7RTwS85QXKaZ7HJKwYyD9eU58+3ZWWJ0b5dWFMv4yWM0NjD7RHcuoy/RPOknnj0/l6KjtgwGgdlH1MjyZ1cPeYzBUEBOPe+YIdcfDRV9U+cqeVVPt13GBcAtLFG3XV1ykY6keoVMnpNL0VFbZurJ88QX0PSPGss7FCEH8PRz3P28Hore9ledPADex5p1WmNt0wAiwBiWw4985CNmN+AOFmin4PKM+y4W3Mw0SAAwYnqxUAKgAGa4N//qV78y1k3LbEx7tA1oxPUZN9zBpEGClRkXZSyqxACfccYZXV20aZBy6Qsu1BBKOQU9sEASBas1VlKYmgGKnA+3Z+KccWnGEo419Bvf+EZXE8Q7Q4ZVXV1t9llrNO1BTsXkw2GHHWZy9xJ3jQWWdESwPbMfRmnktNNOM+f+9re/beoBgLHeUxa57bbbzNr+wa2dlFLWqswYYIFGAOO4SXNuCLcQ4qlZrrrqqq4JD9ytmTzgPIxxJMuO3WNlou9NQ4wx1HEkEx6JpMZqahD3yrh3vkOaX9eJBL3+rbRt9crm3wd1Se/x+NWdUnXVlwCkq045qa/DrthfsuAd0vjkY2q3dNo41GNErZczK9KEegMpgrjh0lPPHKjYqD5ectKpUnvfzyQVc15vSu6iQLjTWWHA8XuUGb701FMGLDfaC7zz4gXy06/8bsjDTOokxJEL3A02UN67P3aaEmFtkpYmjfsYgoTCQTn9PccOoeboqWLIq8rUkND2clZPjlxGyoRXqlwntpQIy81SOWuaFI2vkFjz0K5Hr4Ln6We5+3ocTtePe6dpD+C3kI0Fuq/TW/fYmpoaUwTQBKCF6RhgfNFFF8ll6hYMSITBOFsapEwW6FzTIFkiLEikIHZiTQyulcH0pa80SBB1QQJFv2kb6y3xUgB6CL4YK/0nLheWZqcwKYDAhI1QnlhbADSszIBcBEsu+kGXWHgRgKdTrDszgB/XctyecY0GLDtTKjHhgJ4ZD/1G6KNT7Hdm99njds1+xmgX535bZ6SsU6rT1c97jGVoqH2Ox72y9s1psvO59Pc51HZGer1JF543IFtuf+CXlxJPqECKD5k50lXxlvrvG6sTbb389QbZpNb3jZkwyEqjq3h4QljCY3AJ7zmRMJhREsNePjc9ETiYeqOt7DGnHdbrOTGYMU6cOkbChRoL4XJ590dPUxKmobnTe7weOeXcI6XmkMku16JIfV2Vvif1fG8ZjFKoW183ZjBVRm3ZE798qQQLfIMeH5wKs993mgLoykHXzVfYPxrIW4D3j157tJqNBRrQRuodBGCECzBESqxx+8U6DGizqYewHmeyQD/yyCOGWAtrKS6+/bFAW1doYlKzATALfi0L9HHHHWfIn3DhJSXQvHnzhtwXM8jOP1hMIY4CzOPOzHlxH0bIVYzQP1iXsV4D/LEME49MKiiAuZ1QIP0QzMsQglkiMGKl0TegHxDMGgEI48qMizQEVwBmBLCMKzQWaMA0ixX6gbUd6zJt2TRHWIqpz2QEVnGItLC4W2FMAOsf/OAHBsTjvn3XXXcZQA5YzmSBpr+MDcE1fDhLoiWiOSq98uorB8mRR61X3XVbL3Pp9/9n70wA5Kqq9H+qq6t63zu9ZO3ORiCENUHWgKCg4IaICzoaQcERYVDU+SsoKC64jo6j47iBKG64jIPOsC8Oi2wBEiAkZN866b27eqm1639+t/p2v66u7lQ1maS7Xp3k9dvuve/eU6/qve+ec76DsTPQWyy7tlZK6b62dKpkbZlof1DiviINUh3Uez7zYVIn7tf6LpdoR7vE8orFGyXFx9SUEfOWSKSD+/HIqTWQBbXi/fulblVQdtxVrFbgzF/wUEH9CR2SF3b39xo9BLoHpLBYuSD6E5OmHMtEpj4FkclVpn/ZAo23/PItH5MPvvbzGXcWAq3r/u3yjOtlY4XBtgH9XpZKZW2f8ktkfnf1tJfKYMHUrJ7Zpk9cmBc0KSnqRuV9mYRo0TluvJNghz7jxsuch3Pbh1kDOQB8kD4AawVMhwUa8AvoxX05WQCIN998s7GCYhUFEOMKDBhMxQKNGzQCGRYAmH5YS2MyC7RNg2T7Sj3bl1Qs0BBxQdBEjCskXpn2hfZTCeO78cYbR1IEAUBxdQYoWrngggtk586dJk7Ysj5znphkKzZ1EfHGyQJYBbhiuQXEohPifPl8bEwxdQCrCMATizN1rFCHviGAdQtOcaW2aZBoe/Xq1WYCAldshHKAdFyiIfNCSOn0pje9yQD5ZBZorM4PPfSQKQfQns4S6RsU3Hha91dpLPc8naDYkXZ3YzEl1wnny+OPYfXX7e5EOqu0G8iygtG+frXgFmk8UVAJhzJ7KdFbUyI6EeGPTO/75VB8ZDH9zsbVgktcuU/zVWYiRo9az+PXeNZpPvmUybimVDas7LBlQzL31FbZ+ZBa1TMQj8YI1y7vlrLZ/RIPpud2nkHzM65oX0/qFD7pDiQ4MDXgnG77M6lc09I5UlxaKAN9iWd1un1vVBKxCo3Hzom+52m4WWd7hVouI1Jakb4eh/SZ3dtVLJ37K8Q3azCnymENHHd0SMKaXmtXZ6VSJowaP1IpyKdkgqVKznjGMfvFQ6oHTd6Vk+mhgck/uenRxxnRC9x2EayMdvnd734nDz74oHGtdbJAO62FzsHRxrp16wzY5bhNg8S2kwWatDzEzgLOsHyywJiM0AYAF8ZjYnBhgYZ0ir5gDWVxWoDpCyDYskAT80td+g1JF23Rd1yEnVZWy0g9UV+w6pLeyLlYN2RiaAH0kElhgQVoEjPsFK5Lvl4LfjmHtffiiy82ZFfsA/oBolirnTrFMktfsebCGI0OAKVcB2G8tO1klMYlmvYh/LIL5SDQQuiPdZtmG6s0Qv+wCNM2xxFbDpdq2y8Iu+z4bTlTWP+gCz4jlumcA5j+FtVVa+qexMvZ7p118szTC/Xe5J6b3PSG23NnR5k8/OAKA36VUUdK5ias81YPblsXzFL3/UjYPECjqj9uz+FbdEJV2DJRBb/qKyG+yrFhAhNWzOITvhplOdXfwqG4V9mLE2ym6eoxYthPNZuo/g76akcn4LJYXRMPrVRBr5rQi2pD0nTuHmU7japeDjCh4FFyLF9M6o7rkOqlvfoAKhRP2dyJr+GSMzX1lSlz2KY7fJikc5LQQNWs8gP+LqbSVd2cUW+uVOfddCx/2LNt345aBbNl5nk92W8kaYN5pner5bd9b5VRlc/hHecm3aUaq7emUY5v2iMnNO2WIiVXBOQmh47kK3kgxxfWdcgZR6i3nBKyiS+RSSRVm7ljh14DOQvwQdZ5jgW60wDPiVigscgiAFKAH67QdvLA+VHASo1V9KqrrjJEXABIXIpJNwVwhmAKIR0R7M9NTU2GzZk8wH/84x9NHUsyBggF9E/GKA0o37Bhg5kgWLNmjWmbP48//rghH8PSa/uZCQs07tGAfFygbRom2pqpgvW3qK5G+na1mCG07K2Rrs4yWbS4RWbP6UwQETmwsMcTNy7PmzbOVvIzwFripK+0RCqXNc9UNRyUfvurKvWhGFHLo07K6CzyUCwuXgUck81KxjQWa8iw7qLHuFQe6WYascTHULTkCE0PxQsIEwh5CoLhEVDmYo25mkjQIyzG9n6kfpGyF7tZPJWL9EZMcCUUlEek6fV7peuVcunZqvlCVTFx1RmM5IRbe/BY0P2SxgGpPapbLccJ/Uu+AuDa5W5Woxn78lWLJRIayzuRrlK8+V455Vx3M0A7deXTZ86Rx2fGBo3r9Nlvy7HtWj2WrjhGBl9RPhRFvVhzA10lUt3QY1IemS+3LWiez3G1thdK574KiajHlhF9fypZkbsnrZqKTn6dhF74u8ytVkOKEi129hfLvp4yGQj5zfO5WEFxbVm/WXzqHYMUHP2aMcYn21Zuffg0kAPAB1n3ORbovwqM1BOxQKNu4pAtAzMEU5Zcyn4UuAgT+wyYtWzLWHNhdSZOGFAKAH700UfNNoRaWMiZfCBfMVZiwCYAGJfidBilAcCwUkOwhfu1lY0bN46wU9NXxO0s0HPfcJps/Nkf9WU48cMeDPrlxRcWmKWsfEAKNA2NV2ODQ0GfWsgL9TNI/TMza+XRVs2uXMcHdsusFX2y59GCYaZnBWWaVkaDGDDEJSYThjWDy3gCziUmEDicX6Rupwtf1Hcayo8ed5sysUx4aupEdm8fHrplLkYvo7rULWNJSqTtGasvb8McyVf2ezeLx1csnoaVMrTjQXP/5RdoSq6ju41rc7CzQCL9mhYpqGmR/EOKc3XCYFZofDyh5gr11J/gZjWasZdqGqPjTlsmj9/zvPl+ZqKQ4tICOffiUzOpkvVlL/vnC+WVF3aY2Op0Bkv89YUfPDudoq4oU7n6tdJ5790S05A6BGC7fyekVpobXd2ieV4jMQ0HCetz204MmoP6x1tWLpVnnmV3Xb8uPv186fnFtzTcY8D8VtaUDgjLhOIrkLK3J4w2E5bJnTjkGpjM2HDIO5OtF7SkTeSYPZBY99jm5mZTNBPmZds2lkpid9evXy933HGHsWoCHF966SVDAGXLJa8tEdb/JQs01yRPL0RYsEAnMz1zHkCM+3HySz1WYMS6H0P8RRksvJYFmn3AN+cQdJEOo/SqVavMiwr5hBF0QR+c7NQWqCf3y35mpqL+seftmuOAFLs4j9s6M2l9xJoL9cU39U8HBFftGmu0f1+VkpuVpgS/QLnmC88RX2liQmEmjf1g9jXW/jeZ91qNFVRAMVYAalh61So8vADexr6UxKW4LiqVTV0SH9w1troL93rmn2xioscOHT0S4pFnJhZYsz9Wj4lY6u4Fp4yt6ta9Y6+VIX0JdgpzK0U1ISmf32/cnCua+qSkITgO/PLyHKl/h1qHeYHOyVVfvkTjLTMjqcv3eWXlmUfL0mOacgp0aOAszeV78jnHKlhL7976/A8/IjV1MzvdoGP4r3qz/ORTpGBOqtAE5eLQCexBnahmYTv595GLF8ydJ+Unnfyq+5EtDeSVlEvVVV9VVY39rUw9PiVTPfddUrDs+NSnc0cPmwZSm2YOW3ey88I5Fuixn+s111wzAmLHnknskW/33HPPNWzOv/3tbw3J1DPPPGMIrChhrcJPPvmkAZWwVNv4ZM4z0YCLNXG+5DROh1HauiXzWRGLS+wysbyAViYTkP8rFmjbX64x3Umw6GNx4yypn1cg+7Ym4lc5lol4Nbft4gty7mnxwCYpKO2XJW+Ly8bfaTxwEvCYXKceOfKSDn3+lki8f7t+KPMnL57lZ7fuiEjtYLHUF+uEwiSuz6nUsG+gRF36p+aumqq9mXws1KMvwur6WNbYJaQ0SldMzOBgngz0NktJupWyvNxRJy6SK296j3zj47eoZS15kiv14KORmNz862tSn3T50W//7pPy/tXXyYtPb5ZwaNjlPkknZZUl8pHPXSxvuPi0pDO53QWf/bxs+scPSUx5SzKVBZ+5PtMqWV+++DWvk1afhsaFe/U9UbHwWKciM36Ox/N8UvrmNVmvj5k4wAwecTNxeIeuz9YKmGOBHmWknkj71oIbCARGiKGSy+LuDIkURFzECiNYTk855ZQRwizIp5BULNAcB7ACgC2jNHHFXBPBjdnJKA0JmBXAL8L1cK2GbAsQDKhGDjYL9Be/+EVDOkbbXBNyrukuc+pDEtgdlr6Quu8a62S6PdZceM0KUuI5ltP4YMIjpP4ETbMVypNX/pAGaYu69PqKh+SEq/cncrYSlBlqS1f5WVuuv71Xtu1rlHObtkmBJwrH2gEloiQvwVi+PLO/UWbvT6RiO2ClLC9ASqnWFxrFX9IvhVXhlC91ySrgFowpyd32e2dL5QWJ3+TkMm7dr1Q256XqjrupN6ihDRMLdqRivWmPLvJJRNmOC4sKJi7s0jN4el39lUvk4+/6ukRa+9SzYzQohK28/DyZs7hW3vTe1S7V0OTDLl56hDRcfpXs+bZaLtOUqMb5Nyn4pW5OxmpgsLNXIgH1FNT7Eu6OZPIOwC8pI0NRj/S8uEFq5zSNbSC3d9g1kI79/rB3ciZ0wBIkWQZo1jkW6NQs0ABXAC7M0sT74mr8wAMPjPmYsb6SkggLrBWstKRysnLsscfK4sWLDQu01T8uzKQbQizQhgkaF2euYyWZURorMumeALuQXJF/GJAMcdZvfvMbkwLKsjsz2WHbPhgs0J/4xCeEfMssEGbNBPEMhWRRfYdUFGtaJGWCPZDg9pyfF5Ol9W3ii/ZKnpKUuF08hfUjKphzSp+s+FCrFNZENLaXV+XR+55CHrVqcrxycVDB774E+DW1tZyfWC53C5YzJmLu3t4suwLlEoGwaawKRxTEe3NE3aH39JXL/TubzPFYdDJ4MlI16zfyK5WVX/PP73ygUbo3lyqwVT1O8vXm/EBHgWy7e7YhyfLVaix2TowG/vaje+QvN90hNXrDrVBgW64Alxcury52zTZOvU3q+nyMlsnXMt983Y0y2DNJPKGWd6M8+Ncn5QOvu07DazokqM+fiP6z81xhCUswGpT1a1+RkxveK3t35SYFk++RwIsvyZabvylPtDTIoLLf8xs5kXBuMJIva/fXy7Z/+Tehbk5GNRDRia2fHP0PYh4bGq4EoWJUJwGdS8xkGFAd64Pozo/8q+xbu3G0gdzWtNBAzgJ8kD+GHAv0gVmgYW5ua2szgO/HP/6xSTV04403CmRWlrmZHL+A4je+8Y1y9913GyC6e/duwYr7pS99yXxqANQXX3zRAFsLHlOxLVOGnMhveMMbDHkW+Xq/9rWvyZYtW4zl9T3veY/gds1i2amfe+45Y4nFFRurPlZh+occbBbopUuXmnb5Y0H2yIFpuDHU2yF5/S2mZ021XdI9UCh7uyv0IaCxliMMxZxWVmMFxwCTarUo1Vf0aVoA3dcXwtgL94usWDUNR3cIu1Sg9xMgbfg9pGZZUKr/uUU6Xy6U9heLpH+/T6LqVuovj0nFgrDUHDWocZjhsR2MDYqnaPp7DIzt9MHdiwyGpWNn+7AbmkeebW2Qzd1VsriySxqMS/SQuS+9aj2PKfDF5fmVrirpiySsbADl/Vv3SzQclXy/ux+JcbVmxNXrBWl9Xhnet5RLzbIeZXvW+0wnYWCChliMe3awvVBZopX5tLVQSydu4kh3l6nr9j89+7rkTgW/VkqVM+HoIp3UVa+D/qGYRFWFeert49elVFVXYG87Pd6zt1P+++Y/ykVffZ+t7vr104+8KB9+041j9KB3o2g21jHHrFX4nadeK3c+929SVZNLE4eCMCS8csMXja78+j1+cNd8mVsWkHmlASlTxuIEMaB+i/W7HQj7ZVdfmewJlJnfT1ymN99wkxx3x6+Mh9oYhbtwJ66GlDvf/0XlOsiTtkCpzK7oUR4aFDH8IE/SSVSfOX1KLPbXS78ql9z/XZ28djfZYpJ6Duuu/dk9rJ3IpovnWKAnZ4G2zM3f+MY3hJRIPp9PKisrBetuMnNzc3OzIczConvttdcalmdIwSCjwqoLURULjNA2vdLVV18tlCHnLu1ioSV9EkRkn/rUpwzAnDVrlnF/vvTSSw1ZFgAYMPzEE0+YPmB5Js+y02LM9SDTQtzOAj3wPz8Rf0FIQh5ipD1SWRyUiiL9LHTGOBBUIg0Fwrj++DU/aJEvIiWaBD5/JC5TLZm+qITuv01KL/on8fgzI4nJpt+KeGA8eRWcGjVHBc2S1lhBb8F2dXdYklbxbCy09o5HhRBLrGlWAuECA4TZz/doDm9NixRmll5zBacSZvKf++PjsvLdZ6Q67Zpj7b+73Tkno+64Ptn3dK0Zv9evqaUKYxIL60RXSPVoJrtGVcOt2P3gvTLnn/7Z5GUePeO+rbu/+WcJqZXIKbjll2j6qHxlylZVGclXwOEzL8+jJSPBiDx+20Ny3rVvkdLaHIADvF1/xfdGFZTGVmd7j3z/pl/L9d+5Io3S2V+k84GHJNyasIrPL+3VScBi2dFbaRa8s/h9RPiNdIY0zVeAjITaWqXzob9JzWvPNPtu/rPn7y9K2wvbTBaMTftnyZzKnknV8fzu2Xpew+kCA/L8z/4qJ3/qkknL504eOg0k/fQeugu76Uo5FujXymtf+1ohjRApkHBTxiXZKU7m5vz8fLGglJy8uD1zbOfOnQb4so1YNmW75hgPS7twvLu7W15++WUTT+y0ru7fv5/ixtWZ9R/+8Adj6Z07d65hp162bBmH5b777pOSkhJpamoaSdfkvB5lbPw324g9b9ccs31i7TzOuZkm4Sf/qpbc4HDqhMSrnKpaE8JHpa68T+ZW9cr82m5pqAioi3TQAX51pLwElgV1HZfIxqdm2tAPan/jbY9qkJAiN5DDVIR6itxiLfdOpXbW1HnuT49LcABCttSqBPQORP0pwS8q1PTLEuwPy/P/+fes0clUBjKkk4Z9zz5lbsdUt2Qs7JVwr19iQf39TQF+uSbu04ObN03l8llV5/k7n9aJgvFETfxOFioILhpeksGvVQLPiY1/y7mdoo9n//6ytLV0WtWktQ7rJMIfb7tfOTwiaZXP9kLt990vsWH+kxKdgD6qqmNkyADekHIhsDjB7/E1+4WySKw3IO333jdSx80bG36r91Vvgg9mMOKX+19OTD5H1bvDChkiw0pquXbnHM0RXGIORwdDsuF36vmWk2mjgZwF+BB8FDkW6FEl48ZMnKuN2bVnsKrifox1lzVpjbC4Apq/973vGQstTNDk6cU9mRzBWH+xIGPhJZ0Sll1coAGkLLSFJRjZtGmTfPrTnzau18T/co7cwhdeeKE5/9a3vtVYmykPeRZt92rOPLavuuoqA9q3b99uyhLDTP8uuugi2bx5s/ziF78wqZjMSf2TTr+wKM9Ewf0n1rFHQbwaHasGpLutVF+YR3/4Jx9TXAqLQ+Ir0HjNwX6J7twg/hWrJ6+SpWfjMAcNtiQAsD+1VXLSoYNQ9L/e6BLvXDtp0Ww/uW/DLh2iElqpq1mxuthnItzHIfVWQFpe3JlJ1awrG9q+VS23RKTyfWYiUbfS+GpbsMztqIhDBl9+UYqXHcWeKyXQ1ishJbJ6NRIMDMqWxzfKiW/PpZ55/P7npE+tZ5mKV11U1z25SVaevjzTqllXPrD+hTFjqlfujpXefbK+o9aEhwwNP8PzdGIaFn0Acm3h2Hu4L6mNMQ26aGf3I+vGjDYU9cndLx4hjRW9Guo1YDhRegaLZF9vuQyoO7lTIn2DEtjTJmVzZjkP57YPkwZyAPggKx4SJSuDg4Oybds2k86H+FHiWa0ww/vTn/7U7o6sjznmGLn55psNQCQHLSDszDPPNEzIb3vb24wllMLnn3++iYf9wAc+YOpy3VNPPXWkHayMxL1y/qtf/aphU8aS+u53v3uMay8V6AvuwcTKIgBNLLI33HCDfOhDHxJYii+77LIp98U0OvwHcAszc7LYfMCQYmElphzLD37wgzFFcaEGLAOAOY91HQvtP/7jP5pyRx11lCHB+tOf/mQItog1RgC7jz/+uNm2fzhm0x+Rl/grX/mKfPvb35aPf/zjpgg6RC9vectbzD7XQ5ws0JBhnXHGGcaVmthiJJ1+EW9s5TOf+YwwLoQ26utHyZFsmemyHuraJx5N6h4PJyzAlbMC0tXK58nr78Rvyx6NBfYXRjQv5vBDVePgYvu3T5dhHfp+DO7TIMAC8cSCajVTMKwkOGmhDXpqkUkoQQITDya8GQ79IKbHFYO9g6YjWC8GFMyW5Guc+QHAG+cRylurx0BX5ulBEq1kx99Ih/5WDhMFohNcI9PVY0KdWiOsk47t7maC7m3tNozEr/au6NyReHa92nZmev3d2/ennUbKOVaI7do0Fjsn6iikKSGTpUrDmE5v3CM9Gi7SryCOR3iJhixV+EMpWfQj3ePbSG7TDfuhFJMx0SGv7FJeCZbJxONVb6S27hwAnkxJh/BcDgAfJGU3NzcbwPWtb31rpEWsnFglIV8CSGLNRHCzxRJ68cUXj5Ql3hRrIy63pP0BFOE2TH5bwBzpeCBkwp0X6ybnsIxy3pkDt6mpybRDe42NjfLRj35U6BOM1IBfgC0A0wp9oU3SAEHghSswsbgrV640C8cAgkceeaRhbU63L7geY2l1CnG1uCHbXLekJLrkkksE3f3lL38xRRkbgNOCzpaWFjOJwEQAbMy0+773JchBbFvoi/PoiDYtmKQt4nkRtn/5y1/Kxo0bTXwvFmHIuLguEwsIKZbuuOMOwcIL2RZlTj55dAbeulBTjljjv//97wJwBpBjQeYaiLNf5Pjlc4Vl2jlGU3D4z2te85qR+GLYw6ezeAqKjNXR9pGcvlV1PdLfWySRkM/A4FH3SH2N1hllvX2kuHxQrb9j3dE8RaW2GfetvapHrMAqHvXdHQHB5sDEEwkGkYA21LVvpJQCaTeLh+DKYQG49avrWaGmpbDxPdx/VizwxV0aizHlreTlT8ESbytnwTpP087p3TgykoRuVFHcb1Y47dgf3Ryup0HsnkK9t10sfk1hZMmYXo0a/CXu/l5b3ZWUTu1+iqsfanFOh0aNCc8Oq9HRNT+dAGGWA4nHl4ML6OjVPCe4J33Fue/1ge61Q3U+d0cfJE1j6U0WANG+ffuMZRWAaUmUAL+QM91yyy1jqmDJJLUPeW0BqwBDBFInBBCdLFiJAZpYMxHcdLu6ugyIfec732mA2UMPPSSwLWMhxmXXgkLK0xcALoAPkItFdd26dfLlL3/ZEEu99NJLxgqKZRVJty+MwZkTmbq33367AYvPP/+8SYMEEEdHxARb67W1yGI9/+53vztyPXIC0wfIqh5++GF517veZdp64YWEaw8W5PXr18vHPvYxo0OuR1uAUwRLrQXO7HOcCYoNGzaMAGCOI1jrcZEGCP/5z3+WD3/4w+a4bYtJCsCzjSNmsgAAbvtOuXTGaBrVPxaAs3/nnXfaw9Ny7Sku1/ff0ddeOgkILq8e0Fl6jwHBsEHDFpun7qi+AmXWVTIsJwgxA8v3i7e6cVqO8ZB0yq9Wc4vG9IIGBBMzCAizyI2OoDhHOcP2pA/RYbhhuuoprDNrt/4priod43IKcBtUcOtVJcFCDv8aL3qAXhykY3HYytGWU4tq/aguc6sKzbjzq/mtTHYhN3bgEb3ofFbSt3+sDvMK/OKrTpAFjlRy2UbZrHIlAUxMbk116Ezq1MyfNdXqWVWvGuIwHVEiGjX9oUUGglJbkTA6pF8rO0v6qqskqsaDVyO+4XfMV9NGNtQtqauSUHfflIZCCFlxXeJdfUoN5CodVA3kAPBBVaeMAX1YJLEA/uhHP5LPfe5zxuXZshWT1P373/++uTpxp1g2Aao2Hy4ACgAMYMVqSawq1k1AIyRQ2vX8vgAAQABJREFUTU1NctJJJxkXYUDpP/zDP4wZCdfEUonVFVfo97///QbU4u6cLPQF8IsAFImVJU8vLtGf/exnR1y3M+kLlmYs0E4BGLIALAG9yamLsJBb5mb0xVhxR8aC/uSTT5q4W/oKKEdoC/CPRRldkx4JyzcAn3HQFscRG8v7ute9zkwSoB/cjQG6yUJ9C6yd5+wExj333GOANnmMmdi4/vrrTR/QNZLOGJ3tzqRtT55XPLMWSXzX2DgYxgAQ9uYn9H2gMQ0pba/v6DMOVCxrz3uUCVZKdVKp56mRMQIuxOSz1TWIDfALvuA431tFcOw6ZUitnb7Zb3Qect324hU18pTJ++nUjuZmVJXFJmB9TlYSMG/Jce4GHIXzm/Q+SwUzRvXKrTiZxCMhKTtpNBRnsrLZeq6wrEjql8yWnWu3TnmIheXFsvy846ZcP5sqFjz5rJnMInVUJlKgP6iB+5XY7tTlmVTLyrIV+i7Yv2VbStfmdAbM5GHFKe7+Xls9LXzjydK9ZY9OciVPFtoSE68rF86WwkoXe75NrJrDcsZpazgsHci2i5LH1i64vZLCBxAJMdIjjzwyMlysrrYcqZNwaf7CF75g4lkpBPhFALeUxXoJIF29erVccMEFxtrJGmsleWmdKXsoD1Ak9pfjDQ0NcuWVVxrgaK2WpvEUf+gnllraQACOVjLpC0CfMTkXAC7AlD6dffbZxsLMPlZb+ss21wXUAn4BsMTbUu+ss84yVmLqWgItrMZYiikLERaxxdSHLZrJAbZtvDHWWVzOsZTzuZxzzjlmWKkmBGgLcq1kgbiKvrDGug6IJ2abayFz5swx63TGaArO0D990Tq13I++FE9lGNGIQo5Sd1su+3uOkGhw/E8wmsUi7MHSqxMFZp0C/KL3IcUrUc+KqXwEWVNncU2H5lF9dRY3v9Zfou24XYpLNZ5areZTE03pox4feZ70JsGmdo2ZUev0S88WgPBUpbC0UJpPWjLV6llTr3PDdvF3dslC9c4f/0s58TAJZjhNccbmO+7VucMMkfPEzc7YM8Hiep3im/ozm7qhEnc/r+2Hf/R7z9V7MdVEoS2Rep2vYTnHXeLeSf/UWjm8RzP5TTm8PZ3BV0+VBgkg99vf/nZkgUn4uuuuMwRKuPMCjrEMY20EsO3atUuIh4UFGfCG5fLuu+821lncrIlHtQJA/MhHPmJAMjGtCDGquOraOFVb1rkG/EL6hPUT4qtFixYZK+3atWun3Bdn+2xjdQWYAqa5DoD861//ugHAuBEjPcOEDVjF0RHjJn7WxjoDPBHAJ27RuGvjlmyJsLAIA/gRHn64S9M2sdAAeiztxDojyVZqa4VH/8lCG1iH0RMs0IwFvT/22GOmqJ0sSGeMyW3PpP22lzrV3ZlXjKkJ4Lmzp156n3l2ag1kSa2WhyMSHZz6Swngt2tTqXSv25slGpnaMBoDz0h1EURYU33Rjcuskn6p7Xx6ah3IklrRvZukrDaiv8VT0yPx/hX1EYm8+Lcs0cjUh7HqnadrDt+pudT7ivzy5s+/U71ppv4bO/WeT6+ae/62Vn8jQ3KecguVpKkOih2pns/HlpqpRGlft3l6Deow9GbXM1slEPKbMJBML4/1l7q0kRONUuprlQV1Aya8Jl19eDS0pKwwLDX+V+eGnu71cuXS00AOAKenp1dVKlUaJIAZ5E12AQDj9owl9NprrzVWTizGgK7zzjvPAEAsolgbsQ5DvoTlFzZoxMk+zT4u0wBnXH2xiCKwQFvrqTng+GPBLymGAL9YXGGBxhLK/l133TXlvjguY2KOsVrDdAxgfcc73mFckgH1WHMB9sT7Irhl4yaOtfVrX/uaSXXEcVzBraAr9pksgJgKveJ+DfkYAljGwsxYiB0mtppt6xrtJLkCuALGAc/EYyeLBbiweQOg0TG6gQUat3RL+kVc9YHG6Gwbci7cqFnsJIDz/HTbjvb0yd491VOyAuu8j/T3FUpvT4FEdGbfzRLa3y5b/7NGrcCZg2CMGnkawLLz7jLX6zHe3yvnHrHlVViBPfL6Jdsk3u9ultN4T5s+H+JS08BEZKYgWMFvTZ9+BgEZ0nbcLn4FsVf89pMylOFkgnLjyxFvXCGveU/OUsQ9FNi9X4YiUSnSkJDLh5Mj5E/yc1mg57AWv2mYjDeusdiDbe5+zqDHvpY22dNXrhE2XhNNw7F0xDxnVKe7AxXSt7c9nSpZXyayv0UW1fXppGl6ccB56lFTqB5GJzTulsiuHVmvn5k0wFwM8EH6tAClgBcn8ROWSNLwYLkEENk0SJZ52XlpwB8LaYtwD0ZwH6YuAA/SJciSINvCimvTIGFBtZZR6jQ1JVigAYLJaZBwhU7FAo0FOVUaJFL1JLNAZ9IX+pMspAjCDRng72RIxiKMRZs0SLhP40ZMWdyxiYEGzMNkzTYEYghjRN9Ytj//+c8bUEtuXksI5rw2sb+UY5IByzIEWxw77rhEnBVtYUletmyZrFixwlzPxmPbdmwaJD4fJhOcLNBPPPGEsQhTNp0xOtMgbVfiMojHEID8dBd0FQr6ZfeOWlmwMP2XXcBvcNAvLXtqxOMfUvfdzN2IprtuMukfL2f9uwrUTa9Glv1D+i8X6HEo5JF1/9YoscE8GRqOc8/k2tlWtqooJO9YsUFufzYTd3B12VW3tEuOf0EqtL4MuftxGI8lvo8FRRGZNadL2nSSKwGE9Q14EvGoDksrBqS0UlOcgZujORdo1DX7yLmy85g+aXpOw37iSsamDNmTSSgeln3eLvnYp94wWTFXnYuFRu8lv4Lga2fH5Sl9/D+tC7eamabRP6yL1fR7RrnI8uKx9+uQpkNyu5BlYEjJ/17pqpGjZ7UaSzA0E5MJIa4RTe+zpbta9as2TJc/r0d0NayHoxtapaQzIju6KyWmpJ/xcU76St6mv43VRQOyvL5VPR/1Po2M3s8j7eU2DpsG3P3EP4hqB5gCXlKlsSH+9IorrhhJg4SFMJVgnf35z39uQDNpkKzg3ovFEgB36aWXmsPkq8XqiOWXGGIrgCknC3RyGqRULNAAyuQ0SLgXk/IIizDg2LJAp9sXQKa1iNq+2TRIAH1ItojdtSzQNiaXfWJsL7/8cuOyTJokBLdpLMPE2XIewb2bVEXozLJA41rOMafgtvyNb3zDuEnb4+iO/MpWaAtyLdr+4Ac/aA8bi7XdsWmQsMwns0BzXeteTrkDjdG2yRrLrxVnn+yx6bbO1/s5rJ9hMFggO7bWSePcDrUcEZttXkfGdVfxssZ9e6S3u1ha91ea83nqWeCvdTdbrF9J3AZe2SK924pkw22zZNGFqscCJRLzp9YjisNaHOrOl02/rlXXQK+QusY/q3aczt10wFNcJvHggMwqHZD3n/i8/OcLR8hgNF+tHRM/3nzeqBT7ovKWozZJVXEiN3VeWeLedJPunGPNK1evGd7SVAqKolI/v0O62/S7Tnoz/f7qSXMu8UdfidW6yfe+orZPikqGX+z8RZJXkYsVtIpadPwSue3x2+Rk33KZ650lXv2XDwHesDCZGBZ1G9eUaE9HXpY9xZ36jJttT7t+XTZXzb7cdsM/iYDg0xTknqre5W2RuPQrSIPtvVxVWqEbw7fvqN5iESmqVcZ9l0uxMhd3bNhugOwLbXXSUBKQqkJ+9/Q7nDQvk+B28khXsFD29etv6/D3vqTe3c9rewt5Ybn3Jp4tzdVdMqe8V/YEyqS1T38r9bmDy7hPfxdrNaxmdllASguGfxv1/d7XkOCJsW3l1odXAxO/IRzefs3Yq2MxtQIoIn4VN2QAGNbFdFigJ0qDBDuzlU984hNmMzkNkj1vWaCJ/SWu1aZBsueda8ClMw0SVlIIueg/ccm4YFvBUoscqC+TpUGCzIu2k1mgademEgKoIwsXLjRu2DArM8kAqAacM4nwwx/+0DBEYxXGgopVF8ESTy5gJgYgrWJs6ACQ/eCDD8rSpUsNoIY1esmSJcaSTFu4e8P0DGM2QBh3aMoTDwzhGH1AcD3HrdqyQGN9BvDbvlMunTGaxmbgn+IliySsng1IUC3B27c0SHlFv1RUDkhBYcS4WMXjvIzEDfDF5bmzXd2vIqM/N0M6s1+6XFmQXSyVJ62UnqfX6qxwRALbC2X9vzdI3Up1rTq+X/IL9c1OH6SJ9z7VpYKNYLtPWh4vk+6NRcOARN8NNTdj2YrlLtai5t1etFyCnfuNDqoVzP7DietlQ2utPLe3Xvo0do37EFEtmnuzrDAkxzTulyPrOsyLijmpf/yLj7Gbrlx75yzVmapRa5nPH1NLcI8C4HwJDvglHNSXO439J71Zvl/dUhX0+vX77gQdHq9P8pescqX+Ug36rW99s3ox/Ubu6XpSKj1lMt+r4MNbI2WeYgN8e4f6ZVusRXbH2jRKcEhOWX6yVFUN+++matBlx6oaSkysJanLnMI9V+d3Tsg4z45uxzR+uHpJDnTMO+tE2fv3FyQW0uez/g629JdL22CJlPnCUuYPjfwORjSFYSBcIIGIXzPujU7UeDW92byzThhVrIu3ipar16DjdxICxeaqbrNMppa84hIpWXXqZEVy5w6xBkbfSA/xhbP1ck5rrB0jgAgrKpZDp0UzmWgJl+dnn33WWHCT0yDh/kwMK4ANoAXwxV36pz/9qSGUcqZBAtBaFmhcjTNJgwQIhDkZ123Iu3ATtmLTIKXTl4nSIBFrzIKF104GELsLsMaCTpwzbtbk30WXzgkFgCcTCVjKAcCUJ29ucu7cHTt2mDEDsHE1xmrNte6//37Dyk3cMKAZojD02NzcbNqiPcZogTTjZoaePmB9t27LfJ7EJSOA8fe+972mjI0bTmeMpvIM/eMpKjbAzL5+AHZ7ukvNAtjwemP6khyXaMSrAHjsi4sdMnkysYC6WapXny47f/ijYZCrKXuCXml5pMIsBZVR8ZXFJL9oSMIBr4S7vcbim6yvPAXAJYsXJR921X7x6RfI4PN/F094wIyb2fdjGlvN0qfWy0CoQAZ08qXYF5FyJSIp8Y8PM4gXlEjRae52PfWoVcN39FkSevK/9BV5VPzK7MySlhQqYJlzRFpF3VDorLNW68T3Uk3j97R0xwLSHQ3IuuiWCYf+pS/dOOE5N57wbHhUXcfVu2qce2k62ohLZWlYBp56VCpel+BKSadWNpZZ8LpV8tTXb1M9jgoAtytUZJbRo6m3NPuhzD97ZeqTLjua5/dL5fkXSueffqM+4sPW3TR04KtrlOKjFTznZNpoIPXb6bTpXnZ0JBUL9EQjs260gDIEYAigJVYV12ZiXNesWWPyzmLdTJUGKZkFOt00SJYICwAJsRPrW265ZaSrmfQFwOlMgcQ2/WUsiF2zDci0C8ex9CLOMuzbGFx0BCCFGZoFd3HcjrHiIrhrc5w+YGkm9nrr1q1Gb8Tu4v5sZ9kZs20LMirbJmt7/csuu8y0ZXMG2+PmYvrHfmZ23563a47b8bF2Hrd1ZtK6b7N+Pgmj2rhuA4aj6gaE2+RE4FdVIJ7CYul+Zu24+m46ULJ4oagaVMYrEzfnPo0P7t5UJAMtOhuv7s7JwmTDrJVLxKMTQm6W4tVv0li11HO5pQURaSzvk0U13bruTwl+0V047pfi09z9kowe/Ge+X29HJ/zlaHrCdz//uDert0LutcJqjGfxnXf+cdwzwp53rr///e/oM/NM5yFXb/OsHHjqEZlb0ZMR465Vmld/H+eUtkvPg3fbQ65dVzTPltrGAp3YUs+iDIXo1llat6KpMcOa2Vu89v1XSH5VZi7hjZ+6MXsVMkNHlvqtYYYOZrp2OxULNMDs1ltvNV3mhx63XYiUWJN+KDkNEtZjywINgIMFGssn4I4YWAiZyItrBYZiLMbJrtDE3aYCYBb8EvNL/uDXvOY18uEPf9i4KUNABTGUTcmUaV9sn1hzHazM5Nolbhe25p/97GfmBQEgCci0aY7oC1bbY4891sQL48Zt20AHWKphbsbKjUvy8uUJV1BeOmz+X2JxIbPCtRnLOWmVEJs2ClBu27J1mGhAT3wukHGRy5k2LQt0a2urSYPEZAQWYxi8sbhbSWeMNo6ZOlj9aROh7nSXiLqhxxSzAbuG5zPS7jLgF7gHAVS4NX3ip7QvMMMKVpT2SbBbJwsyBh1K4JSvcawFSt7kcvHk++S56Eo5JvqwYducijrWDZ0ki10+kYDe4hpXGY2VSr43kNF3m+/1UFRflX0QZ+XEqQFIGeF24Hk8kZSWlokzI8FE5dx0PB4cVLK/oNSXB6VX41E7BzT0I01LMKBt8ax2KVZX/fCeXW5S24RjXVgXkM4dQxLSNIT65J6w3NgTapzQsgvrA2MPu3zPW14pzf/xa3nlwrPM+8yk2tTn04Lv/kyKjnJ3iM10vGVyAPggfSrWCpgOCzTgF3CF+3Ky4FJ78803G8CFey6AmIcnIC8VCzRu0AhkWABg+kHbLMks0Fg/SQNk+0o925dULNBYVnETJsYVEq9M+0L7yYIVF4s47sI2by/pjgCZkG5ZFmgbT4s7MoAUSzfu3xBVOVmgYW7G4ourto0bdl4TsM41WdBhspBf2SnoBr1xHQQ3bBv7ay3Qb3/7200aJCzG9HP16tUGUAPGkXTGaN2pKU9OYWKNkVSTE+bENPpD/C4P0CG9xyAgSVe0uBFIIjzKzBnp7km3alaWi4dDOvmis+s1AdnfDlELCkpHofoaqOyS1Iv3dWelbjId1JbOGon0NshpTXsyqhrVl8H/3T5f2utzwA3FxXq7NXRB3RIigxqHHk0LBOvjTCe08iTUWyj5XblJrVQ3INwVHmWBTvwG8j233/XE9x2CR/v8SFXfjceivT2S5y/Q0JCgAbMbW+ukR4kX40nxwGN1wzNpSOZqTGZVMbnB9Z4O9I4t4tI9byggx8zulqd2zVMN2PtvYmVgLfZrOMmxc/ZKXjD3+5isqfyaWTLnWz+TnZ/6R8mLJSairUFg5F1HJ2yq3vJeKV5xQnL13P400MCo2WoadGYmdwELIQILtF1I2wOw4eHmZIF2WgudY6YNyJwsULNpkCjjZIHGMgujMpZLUiCxQNiE0AYg7ve//700NjYKLNAvvPCCSSGEKzSLE2TRF0CwZYEGSFKXfpODl7boO4RPNt1SOn2BFAq3Y+dCjK1lUkYnVg/blbmacwjXwzqKRRegC/hFsJC+/PLLBoxa6ynMzRyjP/TPsjdb92bqveUtbzHplZyxzBxHdzfddJNho2Yfwep78cUXj4BfrL9MQgBQEdt3+sg5hD7SNyYcOI7YcpON0RQc/sNnB1BnsWDbeX66bXuLi0yXmBmGMZIfe/uDP1FfTRk9ieUYkJenpBr+WZm5EE3U9kw97tGXO4/Prx4RMWmY1a3f3SH9bk7uosb5Ao1hbZjVY8CJt7Zhpg7/oPa7orFKntg1V+7a2CxBYs/NfTbxJThPuQe3LpBn9zZIRWPuBQ9teauUCVqfIRFIr/phf574u22+03o+FvYq+FVmfn3789bNnljpLj6zZMliHT3EgCx5uniH14ljUZ0QXLRooYs1NH7ovppaGRr2iAJYLNNUMs3VnQrKospczLN29LcSsAbwLfGHtVybCXuwLeZX5b7b6MJbUWXIrk6at1NT8wyqvmJ6R47q0OrL6FL1W1UUVMDcoql8dFJB6+ZkvAaKlxyhZHbFMhjW30tCv6J5EjGLV4Jhn0R8VVJyQo4UcLzmpseRnAX4IH8OTtImgGWOBXpUweT0BeBZhmRchwGruEADuhFr+W1qajL7uEjjmtzc3GxckCGvooxlgaY++YSvueYacxz2akArkxBYzNNhgSa1Em1gIcf9HNZowOuvf/1r+eMf/2gs05dpHLAFp5mwQE82RjNA/YMruBU7kWL3p+O6aO4ciXQmJiYMCNY3YZNTcALQweEEIHFYN/UlsGg+M9HuFt/cJglteE4nZIakXkHtwECB9OkSU4ua1ZZVK0C5vHRQAfCwZU6VXnDk8e5W4PDom5bEZdMDQ/KSWonaB0rktAW7ND2FWsj1PFYMXqABbOFhve7uLZNHt8+Ttv4SdfeNyYIlOTWiAf/CIwXPBCSmcfyD4XzJ1zhqrzJCQ2xnbkpdEe8bCykxmzJExy3Rna9ACpZkkofZXMYVfy67bI2G4jwkPT2948YLKK6vr1PPqPnjzrn5AKENgNfI/pYRNdSV9Wu6s37D7h4IFUoIVnKN9S1QJt5KBXWFmtosWQqXHJl8yJX7JceeIKEdW/X3Lm4mCfr1+90+UCxdukSHv8Pkra0qHtAUPgOjfAl6f1I3J+M1kK+kreWrTpLuR/5XQ0eYlEkYwkZKqoGkbOVJI7u5jemlgRwAPsifR44F+q8CI/WrYYHmI8G9ePHixcb6ai2qWLJxm8Z92rJA29jZb33rW2M+SVzRTznllLRYoN/znveYWGksyVjMLWkZoJr4bVy/if/Feo4AhN3MAl110nHSu269asJCNJ0zBmkYARLbreGNkSN2X9fRkJQdmWOLLVhQJ6GNqjzNs8okQmlJyCwxdc2N6UsJ+ZO9zMDrC56ZZHCokJn6Ik1JlRORI0r+Ig/7G2RQ03K19pXIn15cJkXK+kx6inJNe1SmuRh79RyM0Fs7KyUY9Y2oLV8nFo4o/rPuXztyzK0beaUVeiuqZ0J80EwaQIgVVb1FR6gJ+KLbb/hYLREa4Vt87NiDuT2jgTe+8Vz5p3+6Ur74xa+O0Qh8GHg/Pf30I8Z7a8zJ3I6UnXF2gm132LsKlTCZVaZs7iwHkryyCik//bUHKuaK85XnnCfdD9wtQ8Mu4SU6sVVS0CMLqiYPRfKWlEnl69zNkD/ZDbLwpq/IU6vP0N9MGXlGG083/e1c9uvfi1d5ZnIyPTWQc4E+BJ+LBVRYGQ8k1o22ubnZFM2Eedm2jRWR2N3169cLbsIzjQUat2LcgbGMWvDL2LD+sv+Od7zDMDfj9oyllpRJlr0Zt2pihbH8pssCTdu4niOWCMvs6B9rkcXlOccCndBKSRW5Va2GktfQjySWxItyqoJxKSlWK6aDOCy5FbfsF8a3qx7Gu6F5dZber8CsUNPPYPlNBr9qgxNfqc44d290i6omHGe8r0UayrbJgrldJjbaFhyM+NQiPEv+vnOu3PvKQuMizb4T/BJLvbipXeqKNkt8IJHb2tZ34zrW3yf9+j6MtTy1pPo+J8qHhkpkYOOG1NVyR+WGG65Tz6Rb5STN/w0p47x5c5Vo8oOyb982M6mbU9F4DdS8e43k6TN+qpKvHmKlp5451epZVa/kuJVStHCxTpum/g6nGizP8kJ18y05JmcBTqUfjv1+zdflYY2r3qg8FG1K1NYxWCg7eirksT1z5Dfv/brSKSQ8aiaqnzt++DSQswAfAt3nWKBHlZwOQzI5iBFAKS7NpGOCRIR4YID93LlzzXnKwbDJYgV3MlykBwYGTKqodFigqUubtEc8LjmKaQP3ZxuD3NPTM8LQjNWZuOCDxQKN5TkaTbhuEUs83SWybZOUqFWtf1AJSTJ4mCbGBYFTXIqLYhLtaBOIJNwsQ+0bpayxT3p3qxt8RkzQep/X9crQlsdEVl/uZhVKvP0lnUTwykVvel7+9SerJdCn8ahp3Zdxk6rr7ReoN4OnVNvZIJ757r4fBxXADnlL9LeuX9n4I5NMdI3ecvxk4bI/GPDIwEvr1eXvNaMnc1tjNHDRhW+TY2cvl2sv/LZcce1FcvqFq6Ta5VwIYxSUtOOvb5SqC98jnb//pcSnkCGh8ePX6z2cPuBLunxW7TLhPO8L35B1bzlX8jX+d/yk6tjh8r0m1d6Cr34nN1k9VjUje8HuPtn1+AuGK2FvX5mwOMWj7607Hn5GFr9hNEOL83xu+/BqIGcBPsj6h43ZLsSQfuUrXzG5dGExJmWRFYAOLNAsNgYWRmJYoL/3ve8ZyyNWUMu8TFzruefqD5eSNyHnn3++QB7V1NRk9rmmU/jRh80YCyZpjYhHJsbVWjSdZelLMgs0rlk33HCDSVn0xS9+Ue66664p98V5LcuQjD5wZ8aayzhggUZggbYAmLRPpEGCoIt6jOHuu+8eAYscS7bY0oYlyaItdEg5tiEXswuEV4hlgSbtE4Jr9dVXXy2XXnqpubbVN22wIJYFmlRT6AZ3b/pqWTwph9V/sjGahob/cD1c51msS7fz/HTbjrbuk9KisFrjAe2ZAHbKeqRG87F6/T6JdrqbMTYeU+K3SFD8JVEpmxNI82PWCQR1h65e3KlrBXDdB/YqSbPhGVssHlAdDEWkrDQsH7/iYXOPYdmdTLwa91tZMSif+8TdOhmjn0Nc2fPVkux2iXS0ayqksALgAp1E9OtvbsK6O5FeOB9RMrHeXiXGU8ascEvufkylq0B7QH557W1y1YKPysfO+bJ0d/TJ167+uXz2xP8n1638jDz338+mqpY7phpouOIaKV6emWs9BIOzP/MlKV15ck6HDg1EwnF5aFuTRGIav6/hNRNJRENw+pTY6f4tCyQUSLBpT1TWrcfj0UFp/+Ua8eVNrJ/oQEg6/vQZnbxJcKa4VVfTddw5C/BB+mSam5sNyHLGogI2IXF6wxveIB/60IeMWy6Xw9oI0IJx2ArgjjjT++67z4A0UhBZFmjSA2ElBADDeoybNOcAypYF2rYDIKYd2rMs0PQJRmpSIAHWLPijDn2hTcsC/Yc//MEQUq1cudK4GUPqBZgmVREux+n2BRZorLZOgbkaF2asnQBRwD3lsK7CFo0wNqy3COUgztq2bZuceOKJJp8vFlrcnSG4sm0RQ4WVGNAOezUkVeT5pS1YoFkQACqA+ic/+YksXLhQvv3tb4+4ngHE//u//1t27txpxoqLGnmZuS5rQDU6RYgtBrRyDfIxExP81re+1VyP87ZfjBG3dz4PcinTb8S6uZsd/fP+97/ffLbskwJruou3vMJ0sUoJmXr6Cg3b4YEtwQnLb7WSmBDTGo9GJK90lPxruo/5/6J/Hi8suwnNFZSqPhb0SN++EsVymi7FvJw4X1C0nOrNVxyR0oZ+A35Nn/xjZ5z/L/o57dssqtLgqwT5CGAWUPvXe4+SlzY16HfWI+HI6GPOrwzaWD6OXtYi579uQwL8MkD9jZNCbcfl4gl1SzzERJ+mNQr59bfKq5Zg0nWRum+sctAtQDmsRFkJ0Xu0b9/YQrk96drbJV888wbpbetV6oOo1KpVbZfqpUTND4O9g7L7xV3yLxd9S954zfny7q9ektNYCg00feensvfbX5Kee/8qQ32TTBaqgSCvqFgaP3G9xq2en6Il9x6K6XvVyxeeq2RNC+X+rc0yr6JHFlV1KSlW4r3Gaiaq3hxbuqpkp7rx8gTafvXlsuzW28Wr3Cs5SWggPqRpHG9ZLAXq9TI0dPyEaoG7o0D2S+RnzeJ777PiqWiesGzuxKHXgH1yHforZ9kVAWnJAtDZt2+fYT0GYAKqEMAvca633HLLmCqQOx177LEGiAFWAdUIccAIIDpZsAwDNK0bMNZU3HZhVYaoCWD20EMPyY9//GOTJ5hcuRZg0hZ9AeDCfAzIxXKJ6zH5dYmhJR8u1mFy7SLp9gUw6cyJTF3LAg2ZFcJ4cXEGIF5wwQXmGK7Hlm2ZuNtLLkm8ENDHk08+2VjAN2zYYAAw5Z555hkzkYCFF8ESa625tIXwOXzpS18ykwvmgP6hnNMaDsD90Y9+ZD6T559/3nxm1Cd9EgCYvtBfhEmKb37zm7J//36zz2QBYN5ej37Rxic/+Ukz2cD1i4qKjP6pYMuZyvqH/M1WyHs83cVTXmNehsEMFaVBKQhHJaDMxUOanzHxksxjExmGxbpbpCREpeo2bcN+Y0F9qa5OfB8SZd35NzyYJwXDfEy+oqhUNfdo+hlNp9Cn4ENZdof0ZSTPNyQ+BXZ+Bcn5BTBNJoT5mP59YXH7a4mndI4ButadqUQnCd751uelu2ejvLJtluzdVy49aqGsKB+UuY09sri5XbdHWJ2MMtGlp2y2Va1r17G//2LM2KPKshsIFBtXSKzmhC8AfIcMQZvVeKIK7pKy5WG1dvSLp9Dtd2VCJ72tPXLNoqvG6LRAZ2AWFyYmbJwn7vvhvVJYViRv++yFzsO57WENzAbUnvdm2f/v/yLBTRr2oBPecX22Ih4FvmyXrz5HZn3gCvHPnjdcK7eyGtj97a8Zz6sSnxIChgtle3eVWYp1v0C/20hIJ7wGIn5bRcr9QWXhbpM93/2mzP/sDSPH3b4RvWeNvlgqEVuZEomp51EoCJSy7z2j2gkr0/b8hQlDUOTOC8X3rkf0vk28l46Wym0dLg3kAPBB1rwT9GG1xQIIsPrc5z5n3J0BlQhkTeTZRbBgYgkFqD7wwAPmGAAKAAxghfwJd9tAIGAAFW65TU1NSqZxkvzgBz8woBTmZadwTSyVWF1xhcbKCKhNFWNKXwC/CEDv05/+tAFvpCv67Gc/O+K6nUlfJmKBBhwC2AGMgL0nn3xSSEOE2zPXBpiiNwRwTt9Xr14tzz77rAGx9N+6JQP6Ab5YVwGkEF8B9O+55x6zTVsI7ttYfpHzzjtPTjvtNGNpBRSzWAHgkksYyzBWcfqFZZp+1dfXj/SL9slTjEWciY3rr7/eWKvRNcIYcWUG9N52221jUj3RR9sve92Ztu7apTloNV7VywuvSqG6QrMwcxxSa1vMvCB7zMwys8t+TU2RHG8UCmu+vN4+KZg1dYKTmaa35P623f+wdLeVSV29WnQd78O4RLMcSGKab3DPE21S090jvsqEVf5AdbLxfF7D8RLqj0jR6HubGWZlRVBWHYet7cAS7FdG7VlHH7hgFpeIbHpCPF1bdWIwbyQtih0uaY+imudyUtH3v4LCoAQf/LkUvfGjkxZ1y8lbPvYz/W4rmzsJ0w8gkWBE7vrX/5GVb1slc49K8FwcoIrrTuMK3fxvtwpkbcFNGyTa1WFyqZMuqejIFeJRr7ucjNdAaO8e6XnoAQ1vUGb8ym55sW2WROMJXQF4BzQKJFmgsqQsadG6H7hPGi7TiYX6huRirtsfal8v8d0PmbAbJvTf/K718vPv42rP+9BYEPzmd62T0rLE+6wMtklsw+2Sf8wVrtPZdB3w2Cnc6drLGdSvo48+WuwCMCO2FRAJ+RPxqFawaNpyxH7i0ow11FoZAb+IZYF+3/veZ6ySgEGspTA8sybOFJdf655LHdoG1NrY33RZoKlLP7HU0gaCddZKJn0B6DMm5wL4w9oJMRWphx599FED0AG1WLIB9FwXYAvIhQWaWGcAKONGT1hTly1bZrrEGKl75plnmpRJs2fPNnHFHAOw0hZu1ExKcGzNmjUGrNIngPDjjz8+wuzM9YnRxuLs7Bdgl/ZpCysxY2CNdR2AS8w2/UYAzAj95fM4++yzjRWdfQAz/WXb6tYUnmF/0GPrc5uNFSi564DdEk1NUV4ckkq1DBMnDDBOBr9Y2/rCpdLx2NPJTbhqv+3eB2X/FnV5tnlUMxx9OOiT/kCpdD35TIY1s6t4qC8oTz87X12dp/Y4C4e98uRT85Stc/hFJbvUk/ZoIuvu0wdAQMpr1YJ7gBjq8Y0qsV1ZULzxfgk/+V/jT7vwSNuONtnw8EtpgV+rnqDGW971nf+2u7n1BBrwlpRKyfGrJG/lGVJw0ulSfPRxOfA7ga443Pvo/2poTeL3bU5Zn1RrzmSdlpmwBuB3XnmPUBahbs8jf5uwvJtODL10myaUT+iFcVfXDsgVn/xfaV7SIX5NLeXTCf9ZDQF52yXPy+Jl7aOqiSiPzfofj+7ntg67Bqb2xnDYuz2zOpAqDRIA6be//e3I8otf/EKuu+46Q7QE8AMcYxkGgAGkIWtqaWkx1stzzjnHADcIoQBtuFkTj2oF115nGiSO4wqNqy4AciIB/OLujIsy5E6LFi0yFsy1a9dOuS/J1wIwQlxFSiHinJuamoz1G1B85ZVXmuLE8mJVJTaY+GVAONZx+gGIPOOMM0w5xrh06VKBbIw+45L8jW98w5wDtCJYmgHBgGpcj63On3rqKQNEbfoj3KvRN3LTTTcZMIt1mXhewCtCW7iPoydYoIkpRu+PPfaYOW8nCzgOyGXCwPaL2GX6ThszWYJ796mrWVy6+4uNpTfTsSh+NgQc/YG4dD79fKbVs6p8z3PrJaLxk/t3VkksOnbmOJ2B7txYL7G+ful+5rl0imdtmV3PvCJrNe9vSCcEpiKDWu+5l4+Q3c9umUr1rKkT2fCovunGFMiG1JLLb2HCw+PAA0xYPipnETusnoF7N6b0NDpwO9lV4sUHXhgzMZ3O6OAEWHePu38X09ETZf5w+z1yVPWbZHHJebJj6950q7myXI8C4Li+c1lZ2bhP6koG1IuLpEij33Nyy+fnxWR2aUCW146CN+r2PJoDwOhvaMfd+ndUZxwrLoko4F0nV/6//5WPffZv8r4rnjKAmHNjRK3A8b7cvTpGJ4dx5wA+TYexZ1l06VRpkLCkQZJkBWsiYAuQdO2115r4VKySACaslRAo4TqLtRHrKORLWH6/9rWvyc9//nPDPO2MJcVl+uGHHzbu17hCY5GFBZrYYqe12F7fgt/nnnvOWI5xYQa4E3cMGL7sssum3Bd7Dbtm3LgsA3IRAC/XgDDMCiAfHX3nO98xeX4BlOzj6u10IWb8N954o7H8UhfrLLHWAH7EskDDzOzUtzmpf9DRqlWrjO5xU4cEDP1zPfoFGLZWeQtwmXSg3C9/+Utj0QWQM1FhSb9w3cYyjxUapmuEGGrGxKQF4J9+WkEXgHuENqg3XSXU0aXWIU13EioQf76yFxcHx1l4J+o74FfVKq3dkF8pgU5LIoZ6ovLZfjwyPBnSsr1WCkvCUqOzxunIkDJ0bl43W/ph3lUJuVyP/R0BtYR75fbfrZIPf/BRQ7KWjh5tmZ/ceqqEPfnS3zGzJ6fseKa6jvcmXnj5jtbO7ZGWbdU6MaNukur+PLEMiVfZyBualJVcc1cb8Wo8Zn+3ZpaqmriaC8507emSUN8o6Eh3yINqBeZZN5M9hdId61TLtbd2yTUf+OpI9TVv/ow8+OLPR/ZzG2M1EFV292Q5oWGf9CrR3d6+Ul3zPuKR8oKggt8+XY/3honqxH5OVAOhV8HorLFO8aC67ZfOzqlyGmggB4AP0ofQrC7LgFVnDDCWR2JbYWoGENk0SJZ52XlpLKAsH/jAB4zrLOdw1aUusb+AJJiPIdvCiotrMMRVWD0pY6WpKcECzQOU+FnawxWaeGNcoVOxQGNBTk6DRDlcipNZoDPpi+1T8po+WoZkACxj4HrJAgEV4JOYZMaDVfy9733vmGLE2wKSsQYTKw24xJLOcQQXdIAngjUWYiqEmGfif7G2I0wAWOZr4rGJrSa+l88K8FxYWDiSBgnXZiYTnCzQEGU50yBhcbZjBDhjxcciDADGsoxurQDoLegl7no6S2IiItHD7v4SnQfVh2axulNN9o6sxdWwofdtnuzvrjBkWaYFnexxszhVtu3F2RLsb5f6+V0KJIZ0Imy8ZrASo8NtLzZKoMtBMnQg5Y9vKquOkN+S34eWfRXyrz84S668/G+qP7VkKDCbSCLqLo3l9/bfrpLeQJGSD6klJJXSJ2ogG4+DfIeFzcZm5WpoL5ZAd7E5Gne46nvUcqRffSkq1XCHur5R8EtJTYdkZrqG23LrKs87qs9MdIAVOCeTa8CbrxMzDtm8cadjL7c5TgMTPCMAuuUFCZKmcXWSD7j993FEH1P7Xpvq+pwyP5wjbeU2DqcGcgD4IGkfYIoL7a9+9atxLeJ6e8UVV4ykQcJCmEpwXcaaC2jGPdgKrMMQQwHQyE+LQNSEJZj8v8TGWtm+fSwL9Ec/+lHjRmzTIKVigYaROjkNEq7KpDzCIuxkgU63LxOlQcKqCkEULsGkYwLMA6og/wJYWmEyAJIsQCNCPXRMjC7ux4B5BAsvurC5gzn2+c9/3li6calmvFivkwUm6YsuukiOO+44k04KKzKxv8lCCivkhz/8obESs801mZBwskBjVbfu5Vjy6X8yC7QF4rYcbSFOdm/rup04M/3++qvVqsML7rD0qCs0aWYqS9WdSkFHnhJj2fdofusT73IeCQwWSu9AoQKVUWRXNHv6Wrrt+P4v1/k6+RIN9I1cAktwe0uF1M/rkspZfeIriI5Y3wb7/dK5v0za91YaZuiRSrpROLvRueu67eLqUsmLJixt3T3F8t1/P0vOPP0VOWb5Xr3fPCYuS3mI9PupoVthn96fcXlu3Vz526OLpV/Zy41oPuaS2sRkmOsUODxgT7l64LTtGBk+3+OKWQNSVj0oQc0JHBpUgjvN+evVlEi+QiW+Kw5LvjKUjxePeIrcrUt0UjOvVgpKCpSgLTMrcFnNzOaJGH8/HPwjVdXl8qu7vymXnPdJOeLoZvndff9y8C+SRS36autkcOPLr2pEPoeH3qtqaKZXLtTsFeEpegtpvnlPcd1M10DW9D8HgA/yR4nF1AqgiLhdGJmJTYW8KR0WaFIIpUqDhCXUyic+8QmziZu0Mw2SPW9ZoJPTINnzzjUA1JkGCQZqCLnoP9ZUXLCtWEB6oL5MlAaJ8QMeAawwJMPaTDwuABQrOVZeBIs1bXz84x83eZRhiybvMeAZt+V3vetdAti/4YYbTB3cl2kbd3Pq4qLMpANAGUt4ssAKzXWrqhJueuQBtrHFlMW9HAZvrPiUwcIPqLfnnCzQAG7O2fRGWJ+x5KK/ZBZo6ttyprEZ9qdoToOxrjm7PRhWwN/pF58XNmhN1UO6FAUZUU2pEFbm2KACZCfwpa7XF5eak1c6m3HddnlzrQQNydzojHJE0ybs3lxnFhSCNZhUSBMJ5yuOWjjRaVccry8JqKsu3AYJq1Cf5qb+610r5N4HjpTmBR1SXdWvoQpBY+nt7CqW7TtqFAiPffQNaf260n5X6GuiQfqWnynRzU+PTGDZcrg2ExfMko7k1S/OWdNVUctWa1z6wHhX0sl0iH2oIccAPZmKRs6dcc6Jsiv64Mh+bmNiDVSedbZ0KVeJN66TqlOQmIaIVK5+7RRqZl+VvOYLJPb8D9QBJtXk3wHGWzYvB4APoKJDeXrsW8ChvHKWXstpjbVDBBBhRcVy6Mxra8GeLYfLM+l+yOObnAYJ92eA35IlSwyAAvji3vzTn/7UWEmdaZAAtMQS4/qMG24maZCwtEJSBUAlBhg3ZCs2DVI6fcGlubFxrGUKPWBlhX2ZCQEAK9fDBRhQSO5idIJL9p///GcTN/v2t7/dXP6ss84ybsqkcmLsAODf/OY3xjJ+1VVXGRZoCjJxQF2s7CxcE+ZmYqat2zFu3EwQoCN0hWBddwr5hamPVR890obNG0ybsEAjWMpxy2biw8YK407Ncvnll49MeFx99dXmc6I9Zwyz85ozZbtmwZDs6xkaB2ojsXxl0k33J2VIKpe4O0aw3L9POhTAxnSiYCKZDPxSh7u32NM6UXVXHA89cY/MreiV7Z0V6pI/OlkAyN34yoG9DHiRmV/ZI4OP3S3Fy09whc5SDXKobL75Thv35lQF0jhGjuB44dw0SmZ/kU3P7pSIxvzl6yTq8GPmgIPGa2bzJnd/nw+opOECf77tAfn1D+9SPg2ffOmnV8m8hQmvsHTru6mc/5iVOpkf13eYqY06psSXBcedNLXKWVbLu3yNRNb+h+oyMwCM90z+0R/JMm3M7OGMvi3M7HFM696nYoGeqMPWPRaLI4LFFZCGqy7AGLfdNWvWGMskoCxVGqRkFuh00yBZIqwdO3YYayjrW265ZaSrmfQFcOtMgcQ2/cXtGcIviKecAmC2TNZbtiTYWK0ubDnAMmKPE5uLWGBqdvQP7NEIcbW4jmOhxW3aCq7VHMdC29TUZA+PWeMWTkw3eYHt50d8MWJBs61g+2P37Xm75jgxinZxHrd1Zso6riyx8+e/LHnqBjlVyVOWycaF3eJtfXKqTcz4euixMPCSutUzI4/dJ3MBqNTU9khs/f2ZV86iGoNPPCir5u5VD4Sp3ZPUW6n1B554IIu0kvlQBra1SCSIt0bmdW0NoiMC2xKeMvaYW9eP/OFJ6eiPimKHtHSK3nuUfLt9X6907H0VRDsuUPiX/+lH8v8+8F15/vGN8uRDL8h5iz8ij9671gUjn9oQNz+3V7YO1GoGhsSEfyatRHVSa7PW3fzsnkyqZW3ZeGmTtG2pzChzg75qSrCvQAajx2StXmbiwHIA+BB8aqlYoAFft956q1kAmYAyrMOALtIPYQnFWmjTIGE9xnIICzTgGIvmnXfemTINEkOCBRriJSydO3fuPGAaJAt+ifmFkRkX6InSIGXaF6eKsariUpwMWrGqAmgBmYB6BFIr0hbhRg4LNsRUiLWgMhFAvC3WZCznxDFjIcbCi/UZoA0Z1bnnnisPPvigaQu3aoioiJ+GLRo9pRJcmOkjlmYrlgUaN2xnGiRSWGFNtoIuAeuQc9k0SJB9AZRZLJC25f/nf/7HkKdBoAbwntYS6hV/oYLgJa0aC4jbaaaiNjp1qZy3SOMze9ybDiA+GDBWobnzOsa5lKenUWbzh5Q5PSBDXfvSq5KlpWKBbqlSNvKT5u0xaT0yHebqhTukojAkQ73uBh2Rlt0S6CjSEP/MX5KtzgPtJRLVidqciOx4aY+Z2mpTL2gswAk+hPGaAfhyrlfnwoL6ogx51t4t7mbIH6+lxJHn126Qt73uCvnlv/51TBEmly994+fln//pZmlvy91/Y5SjOz37uuSJllrpVrbnie7D5DrsM3kTUx6FJ1rqTBupyrjtWKi1TbY/1STBHn9aQ9e5bs2jnCfr/3KEDOxy97M6LYUdwkLp+isewi7NzEtZK2A6LNCAX36wcV9OFlxqb775ZgO+cPUFEEOMhCtzKhZo3KARyLBIg0Q/rKUxmQXapkGyfaWe7UsqFmiIuGBXxoJKPG2mfaH9ZAHk4mJthRhbrLGWjRmGZKzHNk6WeF7Gjth4XcojAHR0BQDGxRgBiLIAhK1ARgWLM22hGwSATHyzBdO2LGvijRkrfQIoW7EWZ9yybRok+rl69WpjvXayQGM1xiXapkGCdRpGaojFklmgmeTgs0bIMzydJR5Sq7s3X+YuaZMBndHcv1MJIdIWwO+QrHr9BkOeEx90r6UoPtgnHq9P78t+WbiwVcnaxoYLTKZSLL+A3yOW7dF7Xa1Loel9z0w2loNybpiUbXlDu0SVqfipXXP0pW10Qmqia/g1Zh3L76KaxH0Y1wlHN8vQQL8Madqj7n1lUj2nNyNVGMtvpxLiDfrF6w9mVDdbCw86UiC1qEqK1f20RBcIeXkK2WmGsILePgW/kcSjyZwJZkicla06dI7rT7+7Wz591c0S6BmQCqnTYIek77gCjV/e8me55T9+L+u33yX1DbXO6q7eDvYF1WtQuU22N8v7jtioE4Vxzfc7csOl1E0wmie94QK5c/sCzYQ2pCm9ct9rFBXr1+dtXr48+7tFsvjMvVK7uNcQA3qSbkfKRkN5MtBVIBvummcmHmIDgxzOyTTRQIqPbJr0bIZ1w1o0YYG2C8zLWB4BX04WaKe10DlM2li3bt0I4LNpkCgD6LMs0B/+8Idl3rx5BpyRAokFVmWENgC4xNMC8mCBxgpJX3CFZnG64NIXQLBlgSbml7r0G8BIW/T9zW9+80i6pXT6AinU1q1bxyxYtLHYWqFtQCXHLDDlGDlyIZnCGgqIPFvZobHm4gI+Z86cMTl0sabSrhXGsmLFCpO+yB6jLgAfhm0rkF5Z12Z7DMsuYNmmSmKcsFNbsX2nj2VlZeYwfaQe/ec4YsvxudvPGsIu209bzhTWPxCQEXPMYq3f9tx0W3vKNM5KGXORpcfvlvnLWoYtwZM/TL1KjFWsREQnnr0xwRyrTwtPdZNpx41/8irrJR5O6LFI2XSXLFEXXl9U7xd9G55EOG/KL20x4JeieZWzJqmR/afyCkdTQh07u1Vev3SryWdZoAB3vHu5xrLp8fLCoJyzZJusaGwbUVBeqbuZi30Nc4wuoho73bGrwrj46c/ppMJ58lL3quU3GCg0Zb2V1ZPWccvJmsbKMUMd0McD1uB2Xbp06dBlv0bVdOnjaxT8KjDWt7LKOnffi2MUpzuP/+9aueL912l4k3q8KJPukCeqkwijzxy2w3lBiYQT7wKnHfsO2bEt57Jr9VheV6HM7T7VnUd+8fIRsr6jWkJKrhhOQbDIMc5t6KqS/9rWpHXyTN0ybSMn+n5XWyNxZhNUNj88W9b9qUn2v1wp4X6vTiBqqsKIR1+R8qRjW6lsvG+OrPvPJsONkuf3ib/G3bwn0+3+GTVvTbeezdD+5FigO43b8UQs0Lg6A8gBvqQvwtoKqMXiiljL7wUXXGBclLGY4o5MWiEEgGvBp2WBhl2b+GLIs0g1hEsxVmNAr1NwVZ5MbGoj3JfPOussYwF2slPTdwSG6HRZoLkmluuf/exnZmKB+naMbM808aj111M5V+Ltm03XFxzRKrWNSkD0UoP0dJTq5EpcPyMlwtEHbZ6ZYdaZZr+6TC/dL3Vzu437s6noLxVvY4Lxe6bp4GD015Pvk7yKWhlq322aKyyKyJKle3WSp1Q6VY8RJczAbdIK+iwqCsmsul69f4Ij53gFzF94vC3myrWnYYFId/vI2BdU9cg8JbXapTmnt3VWSpem4IKJvFAnGKo1Z3VzVbee7x3Roa2Y19hsN125LjpyhXTfqa8EMY1bVUtw554KKVLm56Jye78lUpwZ2KH3IxLq90l/T5GxHFulFS5z7/fa6oD1qReulPV/e1l/D0eBGseNWykbE0gsEpPFxzdNcNZ9h/e3tMuadzme5Xrr9frapTo8W+nrEjM0kbyQ9OePehQFevvlIx+4Tv7y4E+NUcB9Whs74uaVi0dAm36L5enWegXBNTJXme/nlwakWH8bkX79ndwZKJM9/SUKgkfhQTwSleZVi8c26tI9f1WlFFR41QMuoYCBzkIDhBN7ql1970mEkTge4HoS77mKE49zqdam57BH7/Dp2b8Z16scC/RfBUbqiVigAZG4Cr/vfe8zoBZLNqAW1mmArdMl+eSTTzZW7Ms0j+/y5csNECb37uLFiR9im5OY9nAdZvKBcqSFIu4aSy7WbqzEgG2OYVG2rszOm8uyU1988cVyxx13CBZigDXWdctOTcwwwhjczALtWXi2xFo3j1ggS/QFefnJ243FqLezRMIhzReqL9AFhREp1nPkC3WCOXQYj4Qlb8HJbLpW4lVNEm/bPaIbr8ZG19YGzAIAjrLoTLzPF1OvAqzDY1+kUZyZaJi33LU6ZODtefOlJPbsGBIs3EwBwizpSET13OudL3PTKZylZQqPPkHi6sViX9vi6k4+oOB2oKfQTGKR/9ek5dIJLtjJIcwadeRNKIWXa3/z0izVUGbDal6mYQ3DITeZ1Jw1i4lE+ylkUjM7y/7Hv/1Kn+9jXUfjOtHa4deY/3jiFTaWlwBwVgN4ZG15Zac8+vAzsvrsk+xh165LS/IkX1MgRRxu4wDcLT0VZjmQYnxat6xU/fdzou8uAzL7qF2yrbXU5EUfqxKd/E/FoaD3a9WCAckPvaLFV46tkts7bBrIuUAfAtVbV1vyyh5IrHtsc3OzKZoJ87JtGzdoYnfXr19vwNx0YoEmTtkSXUH+BcjkYUUMLARfPPjZx3Ub0Aoh1+tf/3o55ZRTzPAYG20glIfkCgANKRXgF8HiSmyx1SXgmtRLyPnnn2/WyX8sOzX1sAAvXbrU9MXJTm3Jq5JfTux1bJv2vF1znDHZxXnc1plJ65D/WB3Q+J8Ob77+yNf1Sf28bpnd3CE1ahkuKhkPfiGF6OuqFk9B6Uwa9kHva7BNY4nGY1pzHUAvrs7kry3UiYRU4JeCcXU/De0ftXwc9E7OgAbXPohVf3YAAEAASURBVBWUWKqXjgz6Tuzw2qfGvmRnUD0riob3KmmTxqXrT1WSeAS36NCA5vtWN+dQf4GCX5+WGQ/SqBvpT7j2JzXiut3Nj2yQEsVn47V0AFV09aqOczq0WvqvP9wnoaD6iyeLKhbgmwx+bbGe7oD89c8P2F1Xr9sfe1aWVAYlX2N5MxV17JUlVUFpe3RtplWzsny85TGpO6JPiqo0fmGiB3jyyNVjpuk1e2Vo838mn8ntH0YNjH+LPYydydZL51igRz9ZSL5gsCaeFzIoGJOJF8bF+corrzQFAYjEIpPPF/AKOzYxygikUnPnJuw0j2lid6y9sF1jqYUxGpdj9E0sLWAWYRsiLwSLcyqx7NSkfoKEC6CNYO3FYgz4/b9igYalG7dwFhsnnKqP0+VYuLVXeveWmHiXqfQJop2e7Yl4wanUz5Y6Ub3/w2pFI45yKgLYCPb7JbJ761SqZ02dHk0bs25Pg4SVtGUqgofqc7vVhX+vuycSwi17lETs1Vl5okzIbN8ylY8h6+psfORlKVcAXJDmbQlQbtCfxQKN1dyjDNI5URf7UFha93dMSRVMOD/9xPop1c22Sr0bt0q9NyA1/pDagMfNcE04XMrWFoSlLi8gPdpGTjQMr32d5MV6ZcVbdqg6POryPMkDHLKxAiVbfN8mKSwNS3zfEzkVTiMNJPxHplGHZmpXrBUwxwI9ykg90WcJczOxtZYhGablm266SVO6jJL5AFR5gKFXALCNm3WmLbrrrrvMJYg3ThbAKm7REFFBSmWt7/Zzom2nQGbFNYgrPvbYY0fcpCHkQgDi1nX6YLNAf+UrXzGkY1wH8G8BPvvTUSKtLdKzW+NgSiNSWKHxgWm+4DEW1N6yvp63PIl2d0l+pXtJIYYGdWIlqKy56lrq8emSgakIPQb7/MYFK9bh7pQp0WBENg/WSk3JgDQpo7M3hav4RN8jclxua6+SbRoP5ytyt9Ut2tmh7n24SXrFn2GKM+5HwG9cPUOiHaPx2BPp3Q3HA+0BM8wafYSQ31dTAqeEHnzt8/VPtfJDagYkU6anNT3X/WzXY+u+Dn2GF0hwEGtb5tLVkdMjWhtsaTXKW1HRK0936QR0RD09DuCb4FVrcbU/LMcon4JpY1/ue20UEditK01DqE4wp16+QbY+Wi9tryQIwmLhxMtQHs903SxvGJBFZ+yTwrKEMSY+mNOh0eE0+ZMDwAfpg7AWQxigkwVglSkLNCmILAs0RFBO5mXaxz0Y4GhZoO016Qcgj7hV4lRxJf7Wt75lWKDf/e53GxZoCwapk8wCTXof6mKp5bq0ZVmgYYJOty9YdbHOOgVQi3sxVlVIqrACk8YJAixcja0ARsmbe+KJJ5ocxpznGAKotSmTSP0EczLgGWuylfe85z1m3HYfF+lt27YZcEm+ZYQUVKQmwtqL7Nq1SxkmEz/0MGCzOGXTpk2m3xxDJ8QrY7WeiAWac5YFmusDrEnbZOubjeE/n/70p43LOrt8XtNdfHUNpoutL8+S6oWdUlwzoCBO34AnEZMLTy2/rS8r8ZMheAq7GvyiqryiEokFB2Wgt0hjpQcnTKXgVKuZtwH8DvgkEsINVV+aq+ucRVy3na8Ws4i+ID+xY75E1IK5qLZzTDzwRArBYry5rUae2zPbFPGXutsrIb+6Rjww8qu3S0i/oxYETzYxY+5H1R7gdyiesB7TTk6U7LCmVPZvTmiiQr+qpfq2NajvweT6hQiLV+V8/VOkasNKbPUMIC6flWOBRnN1DTXqJTY18Ev9qpocczF6KGxMGBe4x1ZVd8n2/mJd1ItLzyWnjAP4cm82lfTromE6w1KUSyuV0ETpXF3zLSWtY1wWr94n81e2qQeRMuH3KtO2/hYWlESlcm6/FJYnWMmHVSieogSRqt3PrQ+vBnIA+CDrP8cCPTkLtAWcWFMtCLaTB/aj4HggEDDgFjZoYn3vvPNOeeqpp+Saa64R8hnjRo2LM+mfANaAYtyYAbWkfCJueMmSJaZJC+rXrFkjEGsRHw1wvf766+XWW281kwCU3bx58wgQpSLA9Tvf+Y6ZfCBW2crBZoFeuHChbdpMEIzsTNMNX6M+ABK//9K5tVoGu4qkcn63IcoRdfnJS7wHKxkEOqSgxvzuK5XelrIRtlhvack0Hd2h65Z3VqPEuhIzwoBgX0FECor1gak65EXFvhDTIwt8Y0o+FFK3Z0jGjOiEkm/eosS2S/+W1lfpPZiYIFurOYD3B0rluDktUuRTW6a6p3l5mxuWmN6TxPsOqgXkud2zZW/PKNAoa3R3+h5/w2zJU8+MmAJgvuBhvccS+hud3OKetKAXlZJWhXtyxJqkk7IFTe6+H4dvNVl66hGy7emt+kIMzEhYd0sBwrbABOtIOCpzjuIlOycFBX6pq6+RndsPzJ+SSlsnrlqR6rDrjlUcoe8YhHXp5D0CsJ1XPCAdoQJpC/l1UibxPCnMi8ksdXmuKQgZb4QRRWnd8iXNI7tu3sirPVqG/JoGMzyaK91frHrTfMCTCqkfG1ZNWiR38tBqIAeAD7K+cyzQk7NAo24Ip3D7xZpL+iJLLmU/Cqy1nEP+8pe/mMWeIxb4Rz/6kbFq4wJtrdmU/973vif19fVy0UUXGcu4BcCPPPKIzJ49Wy699NKROF5yKmPlBTQDygHUpFs677zzjGWd691+++0GZNNHcipjhUfczgLtDY+17AOAWfKVrAmXaK+mPcKtN6rxrWElzgkF1KqkoGNUNC7G06/HcBNyHh8t4Yat0te+Wdpf2SB5yrCJYNGNKIM2usuH9Vlnl21aKVh3ISJi7ZS41y8lp57jPOS67eVvP13+dvOvRiZb9mj6I5ZqfcGbo+575YUhkwIpqPrrCRbquXJNjVSkekpMzqAwGLiPuvB01+nOOeCS4040LNCjxxTc6otxTIOkuQ/NpIxCXfQGCAb8OnVIvTyfXypOP4tN18vKC0+SB3/ygOZHzoxcDeBc6HJvBOfN86a3nS0//v5vlB8j8TvpPDfZdkVlmbzxLWdNVsQ158qWLhzD8M7Acbev099GlgMJ7PDlR+YmttCTZ/Zp+uOXmEg4kN7GnvdK3qK3jT2U2zusGhj7NnVYu5K9F8+xQL/WuHMDJBHy9AI4YYG27szOT/8LX/iC2cViawEwqZWs2EkGiLIApk4WaAAx+6Q7skJsMe7NTqAN+Rbxtvb6MEvjtgw4RwDhP/nJT4wbMwzUlLX12XaKjSu2x+x5u+a4ZYBm7Txu68ykdXjdg2qpxrVn1DJE/6PKDNu3v0x6dlVK57ZqtfiWS1BTqIwFv1pQ1VfgV+be/Tv+P3vnAR9Hde3/s6tdrXqxbMvdkgs2BtNMMQEDoZMQUmgBAgEChDQSCMl7gRD4P0oekAR4SQh5EAIJoYQEQoAYeIANwfRmY3Dvvar33dX/fO/qrmZXK2kly1jSzNFnNLMz99659+zMzvzuOed3qOZayTvmVHUdTR6+gg61vDXVhwzjLpbhxtosaW7I7AB+qdngy5fMcZOSG3HV571Pnq6upDErm3PgO+tz5KONI2XeyjJ5ackkmbeqTBZuGqHgl9+hxHsYt7+9T97PWd112351f87Ze9+kuxo1xGJ7ozqJFQPEfgW/vDok6tAoTC3AOVOnmU23/yufMUF8+Vmqq8Tfye708tnvnNRdEVcdv/yK89QpJsW11o0WCnPzNQWSZ3FDTVWaEirqy0jw3uhGffHDZrJL3boql7r7eW0V4gvqu2Vrtnmns/u6W5v3P6zvBd4kQne6+jSPewD4U9C2xwKdqGTcmH/+858b8Jp4JPaJ2GAEMqjCwkKzWDIq4p5tPDDuy5s2bTIs0C+88IKJBYbtmTq4WANyEYiziD3mGG7UCLHDkFnZvMPEPE+bNs3EVsPGTFwxdRDLTr27WKDNSQbQv/DaJZreqEmBfM9e7GJDbJWQulj5gz5pWf3JABp133e1yZcti3eW9pq9mNy17+xwN/jlW8kNb5by0dUKgjvMJqT1pWVovUljqySneXNa5QdzIV8WL3e9G6HBeb7Yb2bvWhhctZjoXF7XIFyVyaSLqUYKUN6kPnmbtnnETU79DB1WLGNbR/UYBB9YMi2ezcHZnhu3t7y5QHpoQI+rifn+Fp3v3vLG/Pg+N2+01m+XViWw5MZO576OGz2iudK64Q03q67fjd0DwH38lUDMZJdHHnnEuPpi6SQVzymnnBI/GzcFREws999/vyGegogJIIYrL3GxuO5CAHX00UebPLknnnhi3D2XfLYQK5WVlZk2OadTePj+53/+p2kHsIlVlNjZ5Hhb6tCXH/3oR8YFmM+AQ8Df9ddfb9bk4sXduLd9oU2nQCDVlVi2ZYBssmCx3b49FjfpZIFGj4BgyLCw9qJz60ZNfmCIvMgFDHkYglWYWGCn3HzzzeZ7gp0aV2vOdd1118XZqW2/LAs0wBrdQOAFkLb9ohxWf75z2jrjjDPMd3Xqqaea0zkJu9hx9dVXm9hk4pO3bt3q7FK/3G6tqzLuublK3NQzUeZEZZfNJh1Ac6NEd7qbvbhmc4W8XzFJtqmlsqd5bGEvfkPjXVdtDvXsKxiEpaOVW+XwaeulIIcJr56hN6VuMnbMmdM2SbTS3dcjl0ZYfztb9doyYLYH1wrlWaKatsaTdg1UVzfIQiVxqlc38nAnSgX4cmyNooxtDS2yw+V5vdu1F9uqUjbt4aESGRseGdvRxS3OnGxAydimNJVLw47uXXuTzzVYP9dt3Kq/jOrF1sM0Z1yyjRG1HGvd+k3bBqt6ejSu1nrVQ4Y+dw2bXdcg2ABkdU5qrVW0HNbrsc57xvRI2bu5sBcD3EcKLi8vN+ALxmUrgE2A1sknnyyXXHKJiXflGJZNYk/PPPNMW9QAVCyML774oiGHSsUCDQBevHixYSGGIRqgnMwCDSCmHQAv7sHJLNCANevma/tCmwA+CLwsYRQxsizsA0xDLuVkge6uL12xQMcHrRu0m+xCTNoiK4DKLVu2GBAJY/QXv/jFeHkL+iHDmj9/vmGd/uijjwxT9de+9jUZMiRGagORFSRa7DvuuOMERmdyBtMe4yNuGGtxdna2YZ8mTdO8efPM9wBYB9DadEr0C5foK664Qt58803jyk1MsLNf9J8+QeaFSzbfB7mKcedGksd7zDHHGMDMsQfUqt3vJSP2sxHQ1D35xbVSW5mjL786TWygRKre61NUD2dmtij4bXsp0TZ8WTGX+FQ13LAvM1dTQYWj8o9FU+S8/RdKbrBZ2Yu7eLtrU0qTukjP3zxcFmwplSHj2u8VN+gs1Rh9oRwJah6Z0w5ZKA/MOVSJmyLGVTdVWee+YEbYgOYvHLxQJwbz9XpU66fLxa+/gdysENhpcLrRhv78dyoW05m1ukX7gt4rhVNZgWCG+bhU+SMKle9gWCBDctp4D1ArwLdSSbK2RjT9lKo7UxnNs3K8SS2nDrNyQ8p/EJbCaL5MbB6nVvJt0qh/XKdRw2MMo3bMllMYzZPS8DAJipKx5Xi/jVaPwVzua6YHfQbQZupvpHlid3Jvcz9z9zcrYKYOEsiJtWE+uPifcYGOtsWjA4L1Fm/N1Osv2ZzIbyg3dRhNqpAXKZgX2/b+9wsNeE+rPvoaiBlNFoDO5s2bjWUVgFlSEksPAfjFjRcrpVOIWyUHLXltSVlUXl5uDj/77LNmDYhOlldffdUAPwv2sArjQmzTIAHM5s6dK/fee68Q5wrTsbWM0hZ9AeDecccdBoxiuVywYIFgDYUc6pNPPjHWYdyDkXT7whicOZGpC6mUZYHmM/rhXIBlpwAoEcAgxFQIfQS8Y4W2OYHZj5Ucqy55eq2gi6eeekq+8Y1vqOtOi8DazITAQw89ZBZbjjVWelyyb7jhBgN6ncecExT33HNPPA0SkxS/+MUvDDCnPBMFzn7RfwA5ll0mGxgn4Br9I87+89lahtnme+vv4sstktb6WI5LyIPyizWfrcaoNmsMMBJ7YLb9V2BsrL7qMg1gjou+LPuLhsU/unEjf3ihRHXiBU09NH9fmTFqkxwwYov41YwRUiIsp/BCAvBtjARk3pqxsqqyyBzOL+jkDcZZeZBv+wuHSau+XAQyonLRsW/J64vLZeXWEr3vbKxqogL8Gu8Lu/HEEdvl8CmrdZsXFJ/4C92dTgothUaPkdidHQPBhDmgHcQJhA3gje02FmP0hwQKY9dl2yHXr8bvNVI+fmeF0UOVPoOqmmP3NbAY1+hkAfxO2mds8m5Xf84tyNE0ccqFUNckORp7ObFF053pX62/XsI+BXL6jAHw5kVzFIvEJhxQ2ITpnh7thZM3Tq3n3KJ6M/O8wRIM70HAfLKlYmvu97DqNJYeqe35oqt82vBEFUHaPPurqJvcyA1tz2vU1aZnZxHdq0A5U3xFZWbT+9c/NOAB4D7+HpygD9ZgLICwFuNKi5uuBYDk3/3tb39rzg5IAwQCVF9++WWzDwBVXl5uAOuSJUtMHCupgQBUxMGWlZWZ9EB33323AaVOkiga4JxYKkkRhCv0BRdcYECtcckwZ2j/R1+wxCJYPMlLC3gDfF5zzTVx123Ac7p9AaxigXaKBbbsw6oKcMUdONkt204UAL7p+1FHHSUffPCB3HTTTcayypgQdEWsLm7j6BfdEm9t9YrllVzE6BfBFXnSpElmm3/WQss2x2bNmmWAKyRdWIexDKPf4uJi813YfuJq/d3vftdYxJnYIJ0SFl/bL8aJKzOg909/+pOpb93cIQKzccecdyBKtGi8tG5dF38hxqCRndtslojmViX1ERZhPyloFJQw8Zks0aZ6CU49JHm3qz7767bLiIJqWbudkACfvLdxlHy0ZbiMV+biSZqrMV9TUYQyIlKvKXt21GfLiopiWWfS9vCEVbChs/hT8peZbTf/C04+SKJKYMdlBpidNW2lHFC+QZZvHiorN5dIfbOmjVIwzLWYq/HnE0u3ywQFv/nZ7e66EZ2ICExwNwkW11CoKNfc1zGAG7uPjW+zealrf+kz01vmY+xajF1/OnFT5FmJYrqI/f/C14+RlQvXKcFi+7XGEd6ZU0lmZkD2PWxyqkOu3nfklw+W2X+Yqyz5McubJn+T4mjHECmrpLziXDn23NiEs93n5vWY42fKqr8+o/nS7ZUHwFWGd3MPxyar0U/sDnfe0zGtBTVZ9egTDnezCuNj9/kD4h97pESXz45rLH4QBcaUGN8V3/AHNQ3SQfGP3sae14AHgPv4O9h3330TWsT1FUBEjC3WynPPPdccx6KZXBa35rfeesuAQ8AvgsWVsrjvAroAgwCyRx99VGBLxmKIhfO8884z+WypQ3lALbG/gLwRI0YYIieslhbEUS6V4AqMpZY2AMuASCs96Qtg1IJ9W9+uAfFdpUGij7gRQ3xFHmCEccMc/eGHH8oBBxxg9j388MMGeBKTa5mhsZzb+F8AOCDUChZd0iEhgHms8LaPWHER3KOJf77ooouMjgHP6NEyWLPGmnzWWWeZ8oBv8hQ///zzMnr0aLOPSQTKHHvssfH2+e6eeOIJM8GAbgey1GxqlFwFub4U7rqk8Gmfg089Sl6um1uypHHtesnd272MsU0fvCQHT9gsmyuzlQgr9lPcrBbeZTtKzJJae+17sWJOHbJGwsqmHSgd337AZVutSqVdq2zZeToJw2QMkp/dJAcqCGbpTvRW1bzjOTIClk6dXHSztCx7V4ePNcM5a6W/V3rPtr8md64h37blahF2d3ozp3ZOPf9o+fWVDzh3dbl92lcO1eePu6/BVAo65z9PkzmPvhEHwKnKOPeNnjhcDj15f+cuV28Pn7m/esg0qt085qWVqAx910vc0eFTwN8oww+d3mG/W3dkfOYnEl03T92y0iSsU/CbcfiP9J0plf7dqsU9P27nU27P92aQ9iBVGiQAEnGodvnzn/8s1157rQG/kC8BjgFfWBsB0RBkwXh8xBFHmDhWUvIAuiDWws2aeFQrAEgInoiHffzxx81uXKEBecnxp7YOa8AvQB0WZMidJk6caCyY77//fq/74mzfbneXBgkLN+Aei/fvf/97Qy7F+BgP4JR+Ieecc46xhqNDLMRYY2+99Vbj4s0EAAtEVBMmTDDlaQNdvfTSS6Yc4Bjw7BRcyAHFa9euNRZ7gLD9/nBzxn0cPTn79frrr5sm7GQBccuAXCYM0CUxzLfddpvpD20MZCEfYO2qzZoTWZ2n2rx+ejoerMNVVblSO//DnlYdVOWb3v8/mThkkwzNrVf7b8+UySvLwWUbJFvjqls+iV1/g0o5PRhMzYKPpLp+eFsceg8qthUlTVdNw1CpWehuVnLu7eYlC3TSEGtld6/EHfUcCrWIP+CX5uWxsJWOJdy3Z/uSDXLo8O5jy5kSHadxhP61/Z8EcU98i8PHlsgJ3z6621Nbh/3/fOTbGnrT3VRst80NngJb5ss+h27T96ee5VJGAYRB7HOYXpdbPxo8+tjFkfhHqBHmsB8rs3b3EIoyNaVfkMCMRNLVXeyCV70PNOBNNfaBErtrIlUaJKyrWGetAMawegLafvjDHxpLLfG9AKaTTjrJECjhOou1EYCINRTLL4DvwQcfNMzTNsaUNmEofuWVV+Ku0IA6WKCxkAK+k8WCXyysWDxxYQb4EXcMGCaetrd9ST4XMbfdMUFzftywcWcmdhfdAN4vvvjiuBUbQismC7BsQ0qFoEcmAKZP19ygbdYciMgAoZBhEeuMQGqFq7e1CJud+m/NmjVmwgEiLOqdffbZ9lDcGs6kA2Rh9Itx4DrNRAXu1ghx1QBvXKhhgUaIoaa/TFoweWHTOnEMizXu3ggM1tTrrxKuUQCvPs0VO/Jl+IhKE0vZ075WK2lWWAmkm3Uyws0SrSDeV+R0JWH69Uu46wE6uvcOgORp+ugtcuTktToDra5s1TvcrEZpUc6DcLNPtmwYIiPH7tAXtp6pY+M6jRcO+rWd2P3bs9qDp3S0Lhb9GwxG9PexWSf60iVjUnZ3ddXPytJQE/2NjlS6+3p0XhGbF6+XDPVQ+Ex+QBar+2mVEuJwl9snMC9gXK/lIb+MzMyQzQqYPUmtgR367FknFTJCCtQ/QbklHF4KaDWqS4s6l9cXRmTF0vUyblLM2yt1a+7a21q3VcZOrpAdG3yyZlmpvgN2D9zQkF/DRsomb5Gxk5QdvnaLu5TWzWi3FZ8hT85+Ws47/l3Jyozob2DHScO6hoB8sGyURIZ/XU7spj3v8KevAQ8A95HOy9VlGYDojAEmLhRyJpianWmQYIGGedkpDRrDxkLsKa6zCC7R1MUSCsj70pe+JJBtAQRJgwSYA9hRxkpZWYwFGoBN/CztAWgBkrhCAyyTWaCxiianQaIc4DCZBbonfbF9Sl5b8Etb9Lczt2ys1riPMw7iqbGKn3766QnNHXLIIcZVmZhbAOhdd91l4pQB2VZWrlxpdHvaaacZV2bijrHaoj+s6zbmmPIwS7/99ttmEgLdWRDNMZsGie+HyQQnCzSu6840SBBx2RhjywKNRRgAzPmdwJvv0JKocc30a9GXOZ9fY4f0Abp1c5GMHLMzFiKYBuggNri2OkvqamNu6VicXC3R2PhzMsPywxP/LX9+8wCprFN3aHWD7kxCgbBMHbFVTtxneawI/uRhBR4uFlyg+b1rqM+SjWtLZPT4Hd1ek6iN63HLhmJpbsoU45mmbLyuFq5HnYBFQqGwsfwAgtFV6omZ2Asf7O4G/LYVa227rvnodolyTamagopy99U4SrhyKjXwsknTIilxueRl+KRQF78eR1rdfg12ccGEWyIG4K5XEJwjmZIrISVx8qt6W6VZgW+d/sfJt8CXl3KSv4umB/+htnvygJmr9J0mKiuXjJCIkip2JViLy/baIvsevEaLabx1b12+ujrJAD7G7+Lrn0yQj5fmyJePWSoHTd0qmZrmkZSGTGwvXjNEnnh5L1mxZYRcdnTs/h7Awx2UXe/8TWtQDnf3DQoAg8sycanJAusveWhtHCkWwlQCEMSaC2gmDZIVWIcBgLgwYwFFIJACnAHYbPwr+5NZoJPTIKVigSYWNjkNErGtTz75pIm5BRxbFuh0+9JVGiTAKi7BliEZd2EIrSzwZxxMBgBmAY0Irs/omBhq3I8B81aoj46xVnPen/3sZ3E3acpgUSdWGmZohPJTp06VRYsWGRZo4nMRAC6xyVjeEb4L9Ik7Nt8NVmiEWO5kFmis6ta9nHL0P5kFGgswYsuZD/oPgG+FnM/9WQIaf97aBriYRd60fogUl9RIZtsLc9t7XMIQcDgwbs8VuQakmIPo0/EdJlRwyQfYi0UWmdEGdPb4gsM/lE82Dpc3VoxV4iYlzNAjQAx0ykN1eH6dHD1ltYwd4og7ysxWNm13sxcHhxSLX38f9DKTxoYsWbdquJQMq5KQuvLivteG6fQooDd2LTYqa/mOrYXSonpGfPpbGiguMttu/efP1/E7JqUy1aoRCDSox0pAn20a8qChC4YV2qwJl44oUG5OtHzo5FhGUYlbVdhh3CXjh0swO1NaGrkWfZKjmCNHAW9nMmRcLANCZ8fdvH+fgyZJjqaOq69rNGAXwJtKmAybtPe4VIfcuy9Xryslb0IAtMNGVsrH74+XOp2Q5hljwTAZG7g6cwsaZZ+D1kjp6LZnjdb15Xj3NfqzMmLCcInqZNaGnfnym7/OMLuzdDIwEGiV+sZA3MqePyQk4/YdY6t5636kAQ8A9/GXgcXUCqCIuF0YmW+//XYDuizpEq7Olq0Y4Oxkge4sDRIuu1auuuoqs5mcBsketyzQyWmQ7HHnmgezMw0SDNSAPvpPXDIu2FYsIO2uL12lQQI8AvJhSCY90zvvvCM33HCDsZJbYjB0QxtXXnmlyaOMVZa8x4BnXLudrsnoGPCLBR7L6wknnGC7a9boljjmZEZpJhAgvbJy/fXXm7RMgG2s7zBjc05APzqwLNa4njtZoAHcnMOmN6IcDNroL5kFmnPZcva8A2ntU+AaGjtOGpbF9AYI3rGtQCcHwsoC3SRZCjpgf0Z4YQ63qNWjPqQvLfqg1c9W/OqCnjttH/vRlevM/Y6W5sUau98Se5GDwXj6GJ1xV/fm6oaQVCmYa1Dgka/uqEM0TjhLXVOTxRdQPlRlQXaz5O0zTaL6G2qluSmoEzNDddJM807nNprJmYDqjmuRY1yLYdWrU1r1O8hrS/Xm3O+mbe7tzLK9pGnxh/Fh+/WazM5WPepirebsQ1JNdjE5FproXmK7uOLaNiYcPkXdSNt/95KPOz9nar7bg8+e5dzlbTs0cOyph8n13/q1Y0/qzaFDCmTchMQMFKlLumevf5Q+IyLtv5EA29LRC9QbKyTbNxdoGsOgAcJZep8PG1EtOXlNicrRuj7a8CSugaAyth/6hQPl34+9qaqNPZsbmVBNmpfJytPUZjPK4/W8jf6jgZi/U//pz4DvCdZYu8yYMcPkeCWVEDG2WA6tADoBeyyUB3ABOmF4RgChiE09hPszx4gBxlIKeCMuF5dZC0pNBf1H2wBsXJ+tKzQWTHL78jlZKGvTIAH+CgsLjZUSsH788cfHi/ekL7hQEzvsXACG8+bNkzfeeMPExtI+5yO2GRdxmwMXl2ystfQJkios58ccc4xceumlpi/WQssHCK0gwwL80oaT9ZnjxNQyGYDeYJTG/RpG6RNPPNHojvMjAGHcmNnPhARpkViI97UkV9ZVmnHAAs25sJTDwI1eyeOM8F2zXHbZZYZQi3MSo0x5vreBngapYObh5mFpBmv++dRDIShVFXmyZeMQAz42rS+RzRtKZPvWIuPy7AS/VGltCUvuvu5mlQwdeJxaHmPXX7suY8CiMKdJxpVUyZQR6i5fVJMS/FLHl50ngTFTnNVdtx1Q7w9AcPIvW4umj6quzJftW4plswJi1tWVeR3Br2os/6ADJSM7y3W6Sx5w3nFfVHebmKdL8jEAL3FurFlSSc5BR4ivk/qpyg/2fcGsTDn5J2cYcrDuxtqseW5nnn9Md8Vce3zRc/OlTLKNhbIrJYyv8cuW5e7ml0jWD+zD/gmfTd4tuQp0x0/aJntN3yhTdGG7A/jVWv5J+qzKSJw07NCYC3d8838uiIPfVMP3qS/07a9frz+pnu5S6WdP7/MA8KfwDVgWYXICdyfWPba8vNwUBdwCaEn9U6FkL8TAXnjhhQa8AQwBfVgksTZaAew6WaBtGiSIlog37UwsERZEUBA7sYagyUpP+gK4BdQ7F/qL2zNAkNhdpwCYLZP1ihUrzCGrC1vOglW7n/7iSg2oJN52//07pj0AzBIbjE6cAjszAlkWAokVAJdynGevvfYy+7EaMzGBQF6F8H04xfbH7rPH7Zr9AGS7OPfbOgNpnTGU2fVEHXTsf+fHjSXJHzJuqx3ruWdPYPRkCYwarQNOhm5p6kB/vXNOPEdjsr2f8fFXfEdzWvZOD9Qb9+3L01T64C6Wf+JX+LHq9SCLvva9XtcdrBVnXXKiHPPtz3U6PJ7cuUML5L/X/kFC6uLrSWoNzL5rtpRmZMm0ICRYOhnjeAapg65k6d4ZmWpAaAzLnPteTt2Ii/f6Ryam6OyJKvwj3D1Z3ZmuyDd90feOlLxgVON+NdxGn+UsGbodUk+ZGXsXStHwgs6qe/v3sAa8aYlP4QtIxQINYH3ggQfM2QFGWDCxQLImzQ+WYcCbTYOE9TiZBfrpp582FkpiVQGPXbFAW1doAGgqAGbBr2WBPuyww4zFFRdeiKIAir3ti1PFxD9jyU4GpFhVicEFZALqkSVLlpg0UVhs33vvvTgYtxZU9EdaIgjBIBUDsBPPTFokxJJoYT3GbRrWZlIiYTUGZOOKbPP54lo9adIkExcMQzOxxrAxY10+/PDDTXs2zRFliUNmMgKrOCmssKJbQZeAaFinYamGTfr+++83VnXAcjILNO7dWL0RYof7u7Romqdw2Kd5BWPWoJ70l3drlrC+pHDdp7oWe9LeQC+bPblUwqsXK/lN5xMGqceoD9lsn7qsjk192G17h46SDU3FMjpzpwTaXHTTVcHaxiFy8PBR6RYf1OX8WTlqAc5WDw11eezpJan1/CGt70kHDXzxxvPkvt/NlhFNZGJtz7uKjiv03zE/+rJkF3q666C4th2NtY2yY90O86kkIyQz/SWyI9qspGKxmNV8jVEt8msWDdVlNByVj+fEJrg7a8+V+2vWqylXR95uK0lPDdgOatalV9ZlpaLr5son/3hRyvM0vEb12qDvRUwfhlRn2fp+5K/cJFv+9T8y6otXuUwzA2O4HgDuo+/JWgHTYYEG/PLy/4c//KHD2XGpJQUPABFXXwAxxEhYOFOxQMNUjECGBQCmH9bSiPXSyQJt0yDZvlLP9iUVCzREXFiSiXGFxKunfaH9ZAHk4mJtBRduCL4AiggMyViPbZwsscA2XZQFxpRHnnvuObMmVtgptjyxwKQ7Qg9Yn3GFRjcI7sqAWPYjgFrAJ0Rb9IXvAWBKrDOxwbiCWxZo3LJtGiT6CUBnAsLJAo3Vn3PYNEi4c5966qmGWCyZBRpwPGfOHNOPgQAIm3fs1OtGmaC1xzBKpittqtfceQHJyMuUcFW1BIvar4V02xlM5XyRGskZWy91q/N0WFyb6aAOnTgIamzmqEbxNbaRlAwmpfRiLPUVNbKqKk+CeY0yIqfOsHB210yLEottqMuTtQ35Ur9Tv4ch+d1VccVxwhMgfPXpSxz3bHdAmDIs/mCWpuSqkOAob1Im+UKpqaiVNVUNskIZy7NVoSF1jYyo0mqVDRrZ+/1VcnpyJe9zXAP1lXXxbTYCmooPa7CagVNKfVV9yv1u3tlat03j0fWdj0cMD+90RAEzc/ut9dvTKe2qMpGFatR44wal8DjU/EYCekMKep3i80Wl9rW7pcU/TwKn/s31E/5O3fSH7XazVX/ozQDug7VowgJtl7/+9a8G2ADCnCzQTmuhc8i0sWDBgjjgw33YpjhyskATCzt27Fjj+stxFlyBEdoA4BJPO3LkSIEFGndf+oIrNIsTZNEXQLBlgQboURdABvikLfr+hS98oUd9gRSK9EPOBYu2ZVKmr7QNqGSfBabswwILyRTWXLuf8sTzkj7I5tAF9Nvxz507N+7CbPdBiIXgcg3QdbYF8P7Od74Tt7xiSaavlOM8gGtcp6kLsRl1bd/po03lRB8BzxxnP2LL8b3b7xo2acaP2HLmg/6D0RuwzYK1uL9L1mjcdnUcCoJJ8s7LL0tXYss0G/IhnSXVl2y3g1/0Vb96q2Rktkpeea0CCFBHN4pU62YgNyx54+uUjTsqdSu6D6vo6nsZLMcqn/6HsfwsrBgqSyo1tVHEL2HzptdxhOzn+DItt7RqiMZwhaXquac7FnTrHn0eYCqKtnkl2HvXqQ67z6zRs7qRR2urxZ/tWTGdemK7rrpeLj7oGv3BjN3bDaq0Sk13VKPglz0sz947Vx75xTMU9ySFBioV0NbXpO8dtWlDhVTuqEnRknt3VdfFOEoAtD5MX13NteoxygCYkeo6776OaSL2P7LiaYm8cqUSXlVJrvJ1dCbNTRkypLhaWjf8WyIvf6ezYt7+PaQBzwLcx4r3WKB3GibmzligcXUGkAN8LfAbrYAK6zJiLb9lZWUGqGNJBfCSOxkmZ6ynybG+ti2O2YkI59eKtRZQf+GFFxpSLQivbrzxxjjz9le/+tU4gRUu0kwwYIXGdZ1JACzDgFz6jvSEBRr3aFy+sfJaki87RttHa4XmswXM9lh/XIdK8jXuVF/e9MUXS3CLMkFnZES0752Dt7ACDhij7VM3kNOR/Kk/jnV39qlh1QqpWlYroQlKWqcW3dzx9dJSE5DmykxlNUZXVp+xN5WMrLCESpolkN1mddcX6I1PzJGCM68xaXx2Z1/7c9stFZVS+cw/1PW5QLjO1tUVyKb6PBmZUyul2fWSo7mTA8pMHtbrr169DzY35Orx3HjMcEiv3R2PPy7jLzhHAm2eKP15vLuzbw3z39LJLGse0okqM4lAqEIMrMXPrZdmO7Fd25u0Aru6156XzPGT4sXcvgH4PbnwEvOzl90alBrp+LKM9pSiSO7+0cNSUJInn7/oGLerLWH8VTtr5Vsn3Ch5+nuXC3jrxiUBy3qFTmpde+5d8pvnru22fMLJBukHJuhffKJCTj4wINkmZaHqUd/+dXf7Y8aOXS9Ip4obNKXPi3+vkLMv8EKWUFGruoNH5sRSZ/L5iCNXyb+eTZXRolWGl9ZKQaHe8/qTGl35jETLP69kZJ+nmif9QAO8ZXnShxqwDNCsPRbojizQgEhchb/2ta8ZRmgs2QhpkbCq2vhe0jKRqxcAed9998kNN9xg3LmJn7WWXeoR03z++eebtpxAkmMIFlpipXGfJocyYJT45jPPPNMcJ7YYIW4X+cEPfmBYuSHEAhjb/gFMPRZooyKJrvqg7ckZ+6yPRQUeCtzUutsSztBtFrXA6Tb7WADK5i3QVFGCiNZKiajFyM2y/fHHpGZjLL8qelCvPsksxLpbL/lqEc4d2yA56uacN65O8ifq5zGN7eBXy7fUB6WlMiLVb73uZjXKtn/Nlmhzi4zOrVOPyNjkQFgtkgDhd7ePkFc3j5GXN44zaz6vr8uPg98MdVEbnVOj9Ztl23MvuFqPDH7ng3eIL4qnip18Ya8CYdVnq04gxBf9HLuf28Av5RUkVz35R9VlR5BHK26Uh29/RoIhkIbe23p1FkgobnhDc+wrNszGMT3effXDUpPk7utGvTnH/LffvaBWdJ2EVvfxtqk/5+EO2xmK3nY0h+Xjd1bIwreXdzjuxh0f/es9WfhJoT6H7f0a0wJAl+dOwpJYxNT5aGGBLJz9vhtV12HMkQ/u4uEb33/IYevkyFkrdNIgql6ZGAKi6jnYLGXlO+XSy96Il8NaHH7zv9o/e1t7XAM8xTzZzRrwWKBjbNAAVOKULdEVDNOAXGYniXcmVRGzu7ggQ4CVbM21INVpJZ09e7ZMmTLFEGTZOGLn14nrOHG9WIc5rxXaR7D4IjZ3cE1NotuU/QxwtvWTZ6CTXZrtcbumfcZoF+d+jg00aXpvruRko0vnSzKjiL0oY+kF8EY7vCTHRoo1Ka+wVRo+cDdwq3zlZYmoi1T1pjx130186yD+MiOkD9QcfaCqi7RzRh4t4qW6bZm679ZUS+XcOTHFuvT/9tnPS6ve2+PyaoylN301KEmJXotj8molqhNgtONmidbXSvOqxQ4VJN/fjkMpNrmvkaZF7XmEUxRzzS5+7//5+5ekpSkcH3OwDfACelnyDSBuv/ebdSJn7t/eipf3NpTr4+HXpEFTRNWrPjeoZbczQd9Yf5c0NhugDGh+8fE3Oyvuqv0Lnn1PKrc1y0tvTJJGfeb0RF58Y7JUbW+SBf/yADB6i6r7s87yJajwuBOWyTe//bocf+ISOe74pXLm2R/KBRe+YyYWEgrWrpfW6rUJu7wPe04DHgD+FHTfFQs0TMYAwbvuusswD6fDAl1eXm5An2WBhkHYphCyw/nyl79s2Jshflq7dq1hRD744IM7xJ/a8k4WaHL3kvoHNmpYoHE97oyROp2+2HOwhlwKwEpKJ0Ap5yVeGDdjYnIRmy5q0aJFhrEZizExubgRA36JWbaCxZZ8x8Q7pxKsxQBrQOoll1xi8gtDigWrNjG6sDwjrBkv40aXsFX/8pe/NH2z8dbJLND0i7JdsUDj7k3qqVtvvdX0gX5YIG37i5UafbA4x2aP96d1q1p3ogq68nIbJUNnOjuC4O57m5+ns6dNtdKyYXX3hQdpiagCtkhtbLJl65Ih0qzWXIiH0hElPpWKNYVStz0Wl9W4Ymk61QZtmca29HJBvR4PGrq1bZzdgTf8FkQ+U7pRgm2u+40bNgxaHaUzMO5Hcn3GrELoDw11r0fKEBJBvWhDvTSvXZbO6QZ9mZUL1+nveUf9+VSvfl34S5bG2iZ541n1sPHEaKBFLbmb1myLa6Na9blaJwmadB1WsGsXSyi2Qo/FmDb0WtQ46/df/SRe180baz9YZYb/yjuTZOnq9HhGeM2a89YEefXdiabu2g9WuFmFZuytjTs1hUUiIZtVSqm6Ox82c6185sjVUj5hZ4dJa1NOZ7Zbd3gM5VZne3rtAeA+/gYgZrLLI488IrfccosBuLjgnnLKKfGzMVsJCzSLjQ9dt26dAYi//vWvjfUTkGaZl7GQnnjiiYb4ikZI/QOxUllZmWmTczoFKyNADysqABFgBQt0slWVOvQlmQWaND6wH7MGEANAe9sXZ7/YhuUafcCQDGEV4Pq6666LE0BZtuWZM2caAirAPBMFgFQssZZtmbYsGRXbnclNN91k4oZJk/SLX/zCuEtDbkV8r3W5Rl9MQsDWjJ44F3mPAdzECyO2X5YFmn6hG3IY047tF+Ww+tsxnnHGGea7ggUaAfg75eqrrxbGyoIreH+W8M5t4tOYbOJ9hwwBwKXzkmxH1Cr5eXVKGqGzp7y8bF5nD7hu3bJdGTlVjwhupavfHK1WYJ3cSbIEJysmorHBFWsLZdvSkvihFiVrc7NEG2LhC+ggP7NFjhq5XrI0rhf35lTC/uyMsBxeuklZO9vLRGpqUxV3zb7wDjt5oHe13tY+M8HF8DuCuJhSYvst+DX7wi0S3uoRs6GLiq29C/HYtr4ipl7vv+zYUhlzIXfoAubsZQp0Vyk4xiK8Tpelml5qja6bky7VnVs8lnxUV7OtXQ8P/uMQmfv2BLUEE5rkUGzbJvs49so7E+WZufvEC9Rs6931HG9gEGy01mm6Sv8u8Jeo5bi11vt97C+XgganeNIXGigvLzfABquhFcAmrL4nn3yysT7aGFUInbAw2jhUygNQsTC++OKLhgWaFESWBfrJJ580qYIAwOS7xYrIMYCyZTy25wQQ0w7tWRZo+gQLNDGtgDXiZq3QF9q0LNCWMAprMQukXoBDgCFM0On2BasuLMpOwZIKoCYOmFRFAOqbb77ZnJt+WbEsyqR9uuqqq0z/iKcmrRBW72SXY1uPXMXz5883hFWATyukO8KaTNwx+3GLBtAD8BmfZV4mPROWWvpO6idyIn//+983lmHasv0iL/AVV1xhrL9YsxkPOYdtvygHyzRjxJrN90Eu5WeeibF82nK2f9/4xjdMiiQ+W7Btj/W3dUZ+ockRSr8CCh5Kh6klf6fGVBIfaFyeU/UY6xDgtyGBMTFjSHoz0alaHOj7AnqtkWs1Lq0+WTZ3vBSNqZahEyqNRY2YSkM+pMeYZyDmd9vSIVK3I5GRM6Cx824Wn/6mqCtJXAWAXyy7EF2x1LaodV0VqA76khdsUXKsOhlFvHCby66t6FePEDdLRkFxAtYFBEubdTyVd4JxeebSpJwVpY3NKI6RBdpdbl0XKqFVRJnaeyqFQ/N6WmXQli8ckifNmjM+lTTpJGpTEuBNLldQnJu8y5WfyTFds7UdBD89Zx95c/54OfqQFbL3xK2SkxVz6a1vyJRPVgyXVxX8bqtIvA6zCxKfO25UpC9riL6sp74e09KHMo/5stsnr9Oq4xXabRrwAHAfqZY402QB6OCe/PHHHxuQZ0mUAL+4veL67BQAHgzHMCgDVgHVCJZIBBfeZMEyDNC0xFBYhXGlhXH4rLPOMsCMFEH33nuvib9dvny5kPLHCn0B4EI6BcjFckkqJoApTMiffPKJsQ5PmzbNVEm3L52xQNOmFcZLLC6A0ymWbRmrL7pD6CMgGdCczKJs69IObM1PPfWUYXJmP67VsDZPnjzZsDrjjowwVnRBuif0ZIX4Y3L/cgxxgnjbLyYpsCTbtpgocPaLcgBxLLtMNnAdYHEm/hlJ7v8hhxxi9vPv9ttvj2/3xw1fjj4UyaPQJliCh5ZU64xxptTVhxT485PS/lbCy3FWqFldphvU+6B9vwSCEhg6wjbjurU/J9e4myYMXIFu5bpCXZQqJ79ZMnNaJENTI4WbM6SxSnWrs/KpJFia2v0/VdnBuC9YXKSx0DF3cju+gF6XYzW2lwVRo1G3uYGDSnznZuF+bA3HXoStHiy49TnvXXswxdqX4df72t3Xo1VLZlZQ6qran7V2f3fraEvPQXN3bQ7U49m5WTpprr9/vRzAyLHeZAyqKxk/TLYu25SgxW078+Rvz++fsK+rDyVl7UaFrsoN6mPZuzhpryEmUjB+UKtoIA0u9RvVQBpBP+vr7373u3iPsDRiASQOFxdf3J0tAMS1FhdcBBdgwBtA9eWXXzb7AFAAYIAYIBF3WwiZAFSBQEDKyspMbOvdd99tADJMyE7hnFgqsbriCn3BBRcYUIu7c7LQFwAhgksxABDwBvi85ppr4q7bPekLYDU5LtcCSM7TVeoiO1EA+KbvRx11lHzwwQeCKzOWVcvMTDtWAPXE2yYL1m30iwWY3MJYsbF2Y+HFGo2eLQDGhfnKK680EwhYg61Ls23T9uuFF15IaOunP/1pQr8YJ0Ac0EsMtTMNEl4A1u3atjuQ1kxEZAwdJeG1S+Pd5iU5W2eQWbi8jDVY2SYz1ELcWWqkVv0esw6ITQjEG3LRhk/vubwZh0iVEmF1FJ801YTM0vFY4h6f/hYUH3dC4k6XfSqYPEoaledAp8k6Hbm/80NtdVqlcOqYTuu74UDGMJ2Qiji8Enox6FZ1gQ5NmtqLmoOvyt9v/5cE9MeRONWeyPK3V8q29Ttk2Bh3T8igs4gyP2dr/uTE6a30tMk0rV+JsDxR7pWDi2TF3Ig0a2aG3kimshvThtvFpx4uvtGzpHXVs71ThS8ovqH79a6uV6vPNdBuyunzpt3Z4L777it2we2VuE9AJGRPxPRaAUjYcqRMwqUZAiasoojT+ktZ3HcBpIDBz3/+8/LRRx+ZNS69WDid5EmUB9Ta2N8RI0YYgikApbVa2n4kr+nnX/7yF2Nx5ZglfmIb62+6fQHoMybnYl3Au0tdhOs4bsRYShkroJxx426MHHDAAWbt/HfnnXcaQOvcxzY5hhEs5ABdQClEXLgsA4ytvhn39773PfM90T5u0ejQKeQjZgysnW1BsoXYc9Ffvo9jjz3WTHjwGfBNe2yjw4EsxFx29k7H0HCNDgY7zwts6mrehcj2mDV+IOtiV/peeMRn9FrYlRa0rjJiFRxx1C42MrCr5wZ3xFzGd2EYxLHmZGzfhRYGftXI5rUmH7XTg6Nno1JW7aBPmpd+2LNqg7A05E3//tvbEugZ9lWOaL2lWyLyyqMeezGXxfK3V8jQzICShvVcsO7sXLhePWh2wWW156ftlzX2ytGsGp1wIqTTYepO0TY80ewMh/yHJu5OdA9PRy9Rf7b4D7pKn1We3TEdfX0aZXrzu/Jp9GtQnSNVGiQA0mOPPRZfYBK+9tprjWWUdD6AYwAa1kaANARZmzZtkiOOOEKOO+44wyT8/PPPG+tsMgs0APLyyy83IPnxxx83ugTwdcUCTSFAIGRYWFIhd+qMBbonfUn1RXaXuggLN5MBWLyJ38USC9syoB+rLf1yirWco7NkgekZwZX5iSeeMCRTr7zyiomdZr+N/8XdGndwrNZMVKBzrOVMGvA9oRvcnLE0s+3s1+uvv05T8ckC+gvIZcLAskDfdtttBgDTxkCWVnXnDu9UEi99sesMBKczPvIRNi7+KJ2ig7ZMMNsvOqGs0sO35LhGNJ9yjtZt6a2DYLyhAb2RsXNZW1qu3rqOKvjNaRL/tsUDWg+72vnmZR9IRpa+EphJmd5dkxn+Zmn6aN6udmXA11/81nIJZGYYtufMNEeD2pVeUG/nFnn9H++lWWtwF/vklU8kWtcsJTp53NO5wpH6HhFU8Lzi3ZWDW0lpjC5j1Vty5KQ1Egr0fDKA/LazJq8W38o30jjT4C/iH7a/7Cw4XsKR9K9IJSSXxrAmQdv3osGvoAE0Qm8q4lP4slKlQQJcQZJkBcskVk+shD/84Q8NWzPxvQCmk046yRAo4TqLtRGAiDUUyy+kTQ8++KBhnrYxprSJyzRAz7pCY5GF3ZjYYqe12J7fgl+In7Ac48IMcCfuGDAMUVNv+2LPYdekLsIS2pVwftywcRN/6KGHjG6Ipb344osTmKwBm4BLUigBkJPFWrAh0YLwilhnBBDNRIElpIIUC8HVGsEKjmCtZiFPsLWew+YNWRj9YhyzZs0yExU2XhggjWW+tLTUMF3TDjHUTFwwaUEaJKzIVmDFfuedd8xHGKKp118lUlUhPo3fjTYB3noOOADNrQp+Qc8tLmeLjVZXGuBVW5PZi8mEGEDJKgxIpGqnBErcG5/VWqfEYcMbpGndUA1F4KUk/ReT2OSDT4YOq1J2zp6/HPbX+7Q3/YpUbBPRNGfBUERBGK8GXGPp6rJVAlpPHTvUsyMx1rA3fRnodSqVfTjcrPnKVLBfKkWgNOnS2bQC2lbnSFOefzs3J/JixA+4bGP7mu36vNCMAxo7qVOvUqGGg850aFWDVWeMls/Cy0hJyKo99mKdRKiUmROqZENlgSzdUiLhaHqu0AF/RGaM3yiHlW+UaG2hVbHr1+9FL5KRG+bKtLGV3XJLNLcot0ddUO5ZeoXcFHA30WJ/u3A8ANxH30h5ebkBiM4YYOJVSWsDUzOAyKZBsszLzlND1sTy9a9/XXCdRXAfpi6WUEiXYEWGbAvQRhokwBygjDJWyspiLNAAbOJnaQ9AC5DEFRpgCaCzQl+wICenQaLcqFGjOrBA96Qv9hzJawt+aYv+AkRTCVZrYnGJnyaeGgvv6aefHi/KGCHrAlwef/zxAgu0jaG2hWzqorPPPtu4okPwhR6YQCA9kY3zhd0axmkmIHCFZjLi3HPPlfPOOy9OqLVyZWwmme+HyQSs0pYF+q233oq3xTlxuU5mgcYiDAAG5KJbK/THWrVXrOjfufZ8+l216ksIL8bRiF8yAvpCom8kavDuVigXW3gdy4kSAABAAElEQVRF0ToKpN0s6NKvPpJ5yo5dUwPDJq92aShSy/k1RU1eXqOWLlTQkfr+cZNuIVgbPXa7rFmlcaxp6zGqk4lRGTNuu+qTizh2XbpJbwljVdDAjYwagtlhaWlIBwRzzapbYKaGPFiiLNpxufgDGivouJWxX/Lqyy9nRK/PmNY0RlU/a0ldOwqzT+t7opMHofZraZgBtVHZFgmrDmO6dOoIXYZU6aVajrURXXm6VE20/badftAieXr+XvLJ5mEaD9yu25iyEv+HAi0yffRWOXFa7L1HfyQTC7j4U0DfD795z2fksuM+kq98ZqNa1qOSGbR3dUwxWH3rdCJxwaoC+ckD0+Sk8zwW7f52yXR9B/S33vbj/gBMcVl++OGHO/SSWNZvfvObJn6Ug1gIUwlAEGsuoJk0SFZgHQYA4sKMBRT51a9+ZSzB5P8lhthKMgv0t7/9bXGmQYLIKpkFGkbq5DRIxMkCCrEIA44tC3S6fYHUy1pEbd9sGiQIorDaAsQB87gLA1wt8Kc8+yG94nxW0DGgFPdjQCPu3ZBcwfAMuZW15qIDKwBZBIsuY7Fi0yHZOrSFuzMu005CsbWGXCdWy7aFi3QyCzRWddsW5ZjMSGaBxgKM2HKxVkUuvPBCuykA6f4s/rwCxRe8xiE+nWFX64aCMfvTb987Ysdj/wG9CJbfeKokdWV3Mws0+sgoKhFfpjo9RurVk6Bewx9C6p3BS0biyzBlYxJTZDAY1lj2ZvOC3aoxwBlFmprBxeLL05RSTfU6kdYq48s3y9bNxcpKrqmP8DRIqctYeqks1eHwUp3BB/yq+POKzNqt/zIK9XoMZUlrfa25tgDB5J0mP3W7oFN7t6t2VecBjfd3zh0Eho9pL+7SrZKRRR2Al04tmBhfAG93MnSMu+9pq59hyl6coSzQEY2LRvIVhOUpkVCjPlTq9DkU1ksRbQb1wZOrTHch54Wo+/3KSl4wXJ9ZLhd/XrFEaiuMFr6w/1KZOmK7vLS4XKoaYhbJFp3MRoJtXl1FOY1y7NRVMnl4eypLf553TRol6b+yvUYZw80dT06Sh+eMkVMP3SwnHLhNhhY06/6oVKnFd94nQ2T2uyNk4Zp8ycoJyZT9y2x1b91PNOAB4D7+InCztQIoIm4XN2TS20ydOjUtFujO0iDBzmwFayWSnAbJHreuz1hR585tT4NkjzvXAFBnGiSYkYnBpf/EJeOCbcWmQequL12lQQI8AvJhSCY9E+6/N9xwg7GS2zheXJIBxU4WaFyxsa7i2k0s9D333GMYlmHaxq2aiQb6jU6YiMBibpmnsfxi2cVSDEDmvMTnMnYmJLDcM14stxdddJGJNcY1mbYAx5Bx2bZwPU9mlAbw2/RGlIOwjPaSWaDRoy1ndTqQ1lgtQ5P2lsaF77d1Wy3BBrTpi4gC4WQx4FfT+7SSy9bx4ucPhiR7v4OTi7vqc/YBh6vLaSx+FxCGRTesLJ3NzQGzdiqDnKsQi2Vmamoka2nTAhm52QZIO8u6bTtzn89I46t/N8NGNyNG7dQwg6BUV+VIvU4qcO0xMcO1iB5zcpukoLBewxBazH6rr8zpR9pNV65D+86UVnWBtoLOApk6uaWpuMzklZnAih2FNAyLL2USRL0Rsg48KmGXGz/sdcgE589dj1SQlRuSo886rEd1Bmvh6cftK//4+VNS70gnxTM7m8XYz7sf+YQZ5d0XGuQlgtOPkvDmlfEn8OTSncJSUZ8la3YUSn1zzBsrR58vZSWVUqScCE7htzNz+tHOXa7e3mv6eMnNCki1amFLZZb84YUys3SmFL8q8LOnxchSOyvj7f/0NeAB4D7WudMaa5sGEGFFxXKIBRPhR9yCPVsOl2diUMnjm5wGCTAH8MPaCYACmOHeTGolQKnTaknbuPLi+owbbk/SIEEyhdsxAJUYYACjFZsGKZ2+4EKdKg3SvHnz5I033jATAsQlcz5imxkTuYvRCWAScEt+XIAnAgv0ySefbHL8AlzpGxZ3rMkIVm6nAGhJA2VTF3Eum+4IkIvu+E6whgOAcVdH2Ha2BYhlUgPru3Vb5vu0bWEpx02aMk5GaWKqL7vssviExxVXXGG+J/o8kNMgoaO8o05yAGD2xN6CWzWuSJ+T6YlWCU2cml7ZQVrKryQ5GaGwhOvRXwykAXJZzMSB7o2BtxRAA50oOUlWUeKLCrvdJlmHf14a3nxOfM11Zuj686eeHC26aFyvXpDoMIK7fgbM5Km10xrKk6yZp6Q+6JK9GcUaR24vPMeY0afJA+yYeHEcTtxUj4TAuL0S97nwE8/fz116rDxx52xpbuhZaqmguv3OOtMDwFw24/cfL0PUGu4EwOleTsFQUI762izzLpRuncFazpenoSFMQusEoFOK1dLL0r1o3YKR3RdzSQnesb84ukXu29yqXgiqm27kQJ3cLslzd8hXNyraI4c7eR3YI30ZtCdNxQLd2WCte2x5ebkpArjlZiM1D8CYGFhcZgFspORJlQYpmQUad2FIorpLg2SJsNasWWOInVj/8Y9/jHe1J33pLA0Sbs8QfgFunQJgJqYWAXQCgp1EUewH+CLoBkCK2zMuy0899ZQhCXvmmWfMw45JAnRjATbleSFxCrmVEfRHW+gXsPz0008ntIXVHiGXL+RVCN+HU+x3ZvfZ43bNfuKV7eLcb+sMpHVmqT5Mu0+s2sWQ1JpZlKcv1e6OcwsvfUXyRkfVcp74UoLiDOjQywzLcNLlFtcrFrhQlpLE1LubMCfr4BOkIZob14tzA92hw1haLueR9m0wX4MUSOiAY9p3unCrZcNK9YEM6cg7Xo/pqUPraVx/82LrHZJercFa6vSrPycFQ7sme0w19nOv+7LkF6e+nlOVH+z7zvvvcySnqOfxkyG1pH/+qs8NdvWkNb7GpYs1lAFPrLSKJxSiDnUblyxK2O/mD3Vr1suU1gr5/ND6LtUQ1AmH8ToZe/roZtn2Suz9tssK3sFPVQOJqOBTPbV7TpaKBRqQ98ADD5gFkHnXXXcZ6zBMyxAiYQl1pkHCemxZoAF0kC8B1iDWSk6DhGZhgSaVD67QxLF2lwbJgl/iZHE1xpWYfuDC+/777+9SX5zfNBZWwGQy8RVWVdybAZlYaE888URD7vXII48YyznWWgiiALZf+cpXTPwzVmrKsmC1ZgFcYk3GssskAWCbOGgIrJypi0g7hRyrhFawalOHc1Pe2RaxvAiTD5ZRGqtzcltOgI0usWyTm9imQYKtG6DMYoG0aVj/4VJNWRbisfu7RGsqlJyEXvbiaap1cEP1RWJ67e9j3Z39a63Zpmk66iV7iOoiaWY+nfMWjqlREq0MidbEvCDSqTNYyyxumKGkLr17nAGSlzQlTsgNVj11Na5olaZvi5Mv9fTebisfbpFIpbJJeyIFQ/LktpevMZrobBLLqSbiVc+97kvylR+c7Nzt+u39T9pfTvneSeqOn77Dok8naG+c9/+keGSx6/WHAmBmb2lQPhS9TXsCginLtUvdyLaNni7bNFCzVCcLVTdHFjfL+SNrpSgQkWwNAcvU5zigN0e3+Xx0caN8Z2ytROobpeLDTzz99TMNpP+L0s863t+6Y62A6bBAA36xBuK+nCy41BJ7CkDEzRlAfPTRRxtX5lQs0LjyIpBhkQaJflhLYzILtE2DZPtKPdsXACZxq8h7771n2KIh4iKf8M9+9jND4tXTvpjGkv4BcgGYVgCpEHyR+xixDMkQSGHdRZ+MB+EzEwXWhdi2xUQB1mpAJ2Itx7atmTNnJqRUoh3Oics5ll+ECQcmGYhdtjHPnBfAjnAugC0CALdpkADOuGdjvbaM0pTF6o9L9Le+9S1TZ++995ZTTz3VEIvZfpkD+o/zWiZvC7jtsf64bm1qUANwVEleFNB3wySZ2P/Y9whpjs6oJB5y4afWJnXZVZfR3KEtJsayoSJbH6r6ttGl6OSBWjQBv4Es1SOeDc1dz0J32dwgOVhRlyOvrZsox05f1qMRRTSX48sL95LgxKwe1RuMhaMNej0mCPdrd9cjFWL3dayqero09P9JvFhfd///0ZNGyGNbfivf2u8nUrG1OiX4gBM6oOD3nGtPk/P/3xm7v1MD8AynX3e6FJYWyQNX/kkizWEz0Z1qGFyJ2QXZcvUTV0npxNJURVy5j2c2z5am2izJKmg012F3kzK8dhE+0lSjs926Nm24UnsdBx1WQNsaib0P7pffIvvmtciGpgzZ2qyEbaq3QmWFLlcSwUzHnGy4hohhT/qTBhxfT3/q1sDri7VoQr5kl7/+9a8yZ84cA9ycLNBOa6FzpLQBWZPND2zTIFHGyQJ96aWXCozKWC4BTiywKiO0AcAlnpYYXKyfWCHpC67QLE4XXPoCCLYs0MT8Upd+kzqJtug77sYWpKXTF1yYsbo6F4CqZVKmr7QNWGSfBbkWnGOJBZQ/8cQThqALqzcAFBCPxRuhHvXPPPNMQ1z1ta99zeTltbHHti3KYgGfPXu2yYNMO5ShLSukRAJYw64NAGZBL7YNQLXtO/tsKicstliE6b8ta8sBtO13DfEW40dsOXtuvjviolmGDRtmd/fbtb9oqOqGaw0GWGKnee1g6UxixwFuwUxeXrRctufi5y8o1YdlbA4yT/PYFuhMsl8nFWJkYsn6BPhGJZijMeTjq3Qdi1lv1Ykcn7bjdgk1bZWtVQXyf/OnSCMkYgpsu5KwxgQ3aLk5Cn531OTpbL0H2jKGlGp+7+YktXV1b3c8ptRYkjFEQyQ8iWvg3b+/JSGdyC7Q38smzWUb0WRI8OaztOh/LEZDNYXKS7f9Qyo2xph645W9jbgGjr/sODnmylOkUa27EX3ehnnmti12u0bfaH/w5FUy7ai94/W8DX1Wl7Q9IxTINlZnSVTZ3VV1ZknWj91PmebaGPilTLyN5Aou/BxsnKNGgPbfSiLCxuqE9AxlgT60sFmm5CaCX57dWb63VN/64uRJv9GAZwHu46/CY4HeaVySO2OBxtUZQA5wBWy+/fbbMnr0aGPp5qvAouoUymMJJwUSQmolyMDI64vrMy7bEI/BLI17NOzOWMOR5LawJj/22GPmGKRhlrSKHQDcGTNmGBCKhRqAi9UWsEpsMZ9t+Z6wQONqjcv3/fffbyYWOFdyv9g3kKRFNB5L9QIJp4mvVFALyRB5gVMJLs/kC6YswgO20eQYTVXaPftacss0tkrTGLX9Cof04ZmZ3ywt9UGddVeXs+aYTtFdIFsnj3SWOYjV1yEtDWHxF7qbnCS8Za3m/lwlG/1DDZid/cE0mTxyq0zSVB/G3V6vPyZdYi92ylre6pcVm4bK0k3DTS7MDH9EhjV+KJEdm/Qlz7269BWNMCzQqS1DsXvXceml3OT9rsWf+BuesqBLdr720Kvy0FV/MqPNCfhkjE5Qv9ekaabaxj9OY0mGBdQ1VYWJ6dtOuUX+6+1bNBTbI8xpU1HC6owrPy+P3f+S7NxUaZigA6pJIAW5ldXGKWPKSmX6kVMT6ngfVAPj95PIu/Mkg2ewguCWhpD4mvSZrN5YZtKVC5LfSFVmVDMRRFuU0LItuwP6iygDvG/8dDZdL9HqZZKf8aQ+T4amrYuMUKsMGb9OImsel0DZ2WnX8wruXg14ALiP9euxQMcYqTtjgQbQ4iqMtRZ3XyzZWFqJ5wVkYoXFqkpqJOKcsZrefffdss8++xgXbayrkHkhuGQDULFyT5o0yeyD7IvcwZSzrtLE3JICadGiRWYf1mlrpTWV2v4BsmfNmiU//elP47s5N/0qLS01VngOMAY3s0BvffkNyVCw61fggPDCHFCQJrpYkGH2YxXWY8kv1FF9mFZvapSWSrVkFrW7w5vGXPRv3ZvrpEhfNIJq9bWCrjJzFezq0p3gvrt1a5EElq2Rwsnjuys+aI83/vtJGVW4Uxb4SszLcFNLUBauHW2W4rw6IbVHKNgijbq/oSlTcJd2il8B8siiCml47R+S98VYyILzuFu2K998R5qaQ5KV2dDhnk1XB3iGbH/jIyk+8UvpVhm05cItYfn79Y8njC+gN/hhWfnSrEjDrz+OfLaCF1Hl5kp587HXZdYFR9vd3tqhgaKSfPnLO/8tJ425XFqULbuuMfY7mackWVPKS+Whd35uJhIcVbxN1cDqtT4ZooCWyT4rANxIE0v3ky0RLbtyXYYMt5VdvI4svlMyfLUydmZA1rxWbKzpXapDny95pc1SOHKnRD65XTLGn6nXaGpjQZfteAf7XAPet9DnKu3YoMcC/VkTb4uFljhlACnsy5B/wbLMgx8r76GHHmoeXrhYE3dMKiQIuU444QST0gjNcqysrMwo+YILLjBrYmqt2JhddG5dvR999FEDfokFdsZo2zqsyTmMu7ZNdYSbNOD7hRdeMDHYtGXJq2y7tn6yS7M9bteUY4x2ce63bQyk9Y65/5aKqlwzK5zcb97nsPSy8BvPZ6cAkMORDGkMZ8uOV15zHnLd9pp/zpWli0bpdda7n2HyLy9fNlI2vPi663TnHHDj6/+U3GC9jBtWkfCCR5mK2lzZsLNIVm4ZJht13RH8RqVs+A51ga6Xxnn/dDbruu1tz70gFduz9Xcq6aZNUxOA36rKXKl4dV6aNQZ3sRVvLpem+ljmgOSRZuqPoxP82uMN1Q3yyh/n2o/eOoUGho8ukZe23CffuemrctzpM+WUc4+Un/z2EvnTmzd3INdMUd2Vuz56aYVsrck1oUs9VQD3NXU/1jbcLrzDRbe8omqIyoTjdkrO0HY36JS6UfCLxf3ACzeY9yEJ10lrjafHlLraAzs9C/CnoPSuWKA5PTcV1sy33nrLrLtjgSb218kC/eCDDxoSJsClFVigcRWGBZp8uMTAzp07V0hDlAqAAfiIucWlmPzBhx12mBBrDAs0bNLTp083YJBtJyN1On2xfWINyRd9h1UZ4Mp5scgiWG8RywIN+IQc69prr5X77rsvfoyxIbQ1bdo040p95ZVXGpZmXI0Zn43vxWUaJmlSHJHuiFROCKRV9AHLMa7PAHIE123GR0w0YyMW2RJZJbNAk5KK3Mi4OdtYX9pgTJYFmv4T10u/AMosAGlL1EV52K2rqqrYjFuZzYd++C9S3yDRxiZpaAyZcWZnN/coIxKAePvOfCXPqpemTbFY7n44zE+lSw2bt0nFtiEyeswOvT41/jedPKuOni1dPFLqq/1Ss2KdY6/7NiMVW82gD5qwTnZU50pVfZb6HnQ/qeCDyE1jsw6csN7Uj+509/XYtHGjTsYEpaYmW71e6vU3Lf1ryUxsqetkbW22+DJr1SqirvlBd79erP5glTTWxohy0tek5qL/OHY99qSO28oOGV4oF/zwNPnpFXfJWV8/WfabMcVtKujReGs375T3m0drLlrNOuCP8Uek2wC/A++vGy3RULuhId26g65ck+ogGgO9TPDP/N46WfjXUtm+WA0CxFXHuSeUHyU7KjklzXLABRpak6lAGFHPj9bqpZpTeXLss/d/j2qgB4+4PdrPfn9yawXEwmiXX//618aSiaUTgEXKIsQyL8MCzWLjQ9etW2dAHfUgswKIWeZlLKSkBsJNd/HixfK5z31OIFYqKyszbdq4V/phLY2wQNMOgJZzAgr5zHErti/JLNAAOFigKY819rnnnutxX+w5ktewXKMPgCXEWlh1r7vuugQCKMYKkK2pqRGYri0B1x133BF3babdm2++2dT78Y9/bBiiKX/OOecYkM1xdEi8MWAbcrGHHnqI3cKkBJ8h+0LQJYK+Ab+MGzdp2oPUDKEdxLJAA8RJF4W7N+7WySzQdowQbPG9wQKNOC3WfL799ttNn+k3oLw/S7NOKPhDsZi17TsL9IWZ66n7HlMGl90Nm4s1PVXsxbhp85buKw7iEi21RK355L13JqlVHDK69CxvYc3JuEQtx2tWx4hNGrbtHMRaSmNoLTErG/FtJx64SDI1rs3p6peqhcxAWIbm18mXDlsQn8CJNiazIKeqOXj3hdsm4Sor8nVyK3aPpzNaLERhBb+bNpZocZ9kKPlfi8MrJ502BmOZik0VGk+pyumhQJTTmeW4h00N6uLr12yWB+5+Uj532DcH9Th3dXDNdRrSoI0QAvLS4kmmuWgaz2zKNOl9/c/5e5u6vDe2dOLRsKt9HCj1W5s0xZs/0WV837O2yMHfXC/ln90pQ6fWSsnkOhl7eKVMP2eT2Z+Z1+52rjODqtTtA2W4g76fsTfRQT/M3T9AABNiwZLzjJAe9ZQFGvBpWaCffPJJAxKxYF588cWmaQiknCzQ9nz0AxAMkzNxqsTH/vKXvzQs0F/96lcNC/RGnem3kswCTXof6mJd5by0ZVmgYYJOty9YdbG+OoV4X4A1MbSAT8Ae4BbQCYi0QgwwoPC8884T3Jw3bdpkwDj9hjDLKaQ/spZZ9mNZ5TtgXPT7tNNOM4utg1X5xhtvFFJK/fCHP7S7jZUboH/EEUcYCy1WWY5DYIUL9bnnnhuPG0YnxAUTw9wZCzTHLAs0kwwAbCz7CPWdgoX7iiuuMLsuueQS56F+t52plvR2plifbN46RIoKaiU/D0sHrs+JXQb4RtUFKKxAebu+WFvwS6nQSHezxQbzciTS0KjA1y8vPr+/7HfAaikdUamTLxCGJeqRTxEFvsRiLVk0WtatbWcLzx4O8HCxBEP6UsFkgr6b6JveFw9dIMs3DZOP143UyRl0BvGVAjPIsBQkZ6jld9rYTTJp5PY4+DV1s9zNTB5Q8r/mbbGXs21bi6WwqEYK8lWvqrdU1yP3Nvpt1Byh27eTxi42gRNVD5eg/k64XYpHFSvBkE5s9RAEkw84Mzv9CQg36nnVwnXy0l9el0On7CMlxYXy+C//JZ/96kwZOnqIG9XR5Zgzc7Pjx+ubM+XpBXvLZyaslvwsvU/1WYNXllO4r1t0Qra6MUveXDVOw5VigI/cysEc/a11sfiy1FOwzQLsVAMxvnmlie+7zuPxbb/ihKz2Z3d8v7exRzTgAeA+VrvHAt01C/S4ceOMxrGmWhBsJw/sV8H++vp6Ex/8/vvvG2ZmC9pJFUQKI9yoySFMCqHy8vIEFmhIsADq3/jGN8wkgW0XYi0syKmE2F8b/8txADjAlbbpA/HB1k26r1mgmRiwYnMZ28/9bZ2RramdskLqBt3u2ldZnSfVtTmSk90ouTlNMQCnL82AtSYl2KhXxslGQ7TR/qT16URI1ij3Mu7yveaMGiaNceutTxZ8WC65OpEwbvxWGTESorawsQrzglJXF5LNG4s13/UwnUxo/9n2BTKkYGL79dPfrpdPoz/+Ik0nVRsLo+B8gOC9Rm1TgKsu5npdVqtLNMRYISXDKsxplKJcde9tvxTjXfQPcff1mDVmjNQvXxHXR1VlvtTW5GgYihKJ6X3t15dlAC/M2qwb9L6uqc7RydlEi4hfuR7c7v6MEstnTJSsvCypr6yP6zSdjXHTxxnvp3TKurHM769+WGbfN0ca6prM5EKNVMkD7z8u/6v773jtOtn3iCluVEuXY84ZqpNbdbFndoP+Fr68ZJIM14nrcUMqpFQ9YTIzYm7RzZGAbNHn+dqKItmqa50yjLebU+JewkqrBF+oRFrhN7E7ergm8Zkvf1IPa3nFd5cG2t+kdtcZXNauxwLdNQs0lwNxyLfccosBuRBjWXIpe6ngLozrsZON2R7DVRrLOORYuJYDjHHxnjBhgilCHPFLL71k3JWxDEOGRftYzInvxQqL1TZZsPhitSYe+M477xRSIdl8w5TFskwcMeJ2FuigWvBhcHY+BLBi1iq7Lks6ElWCsYKDD0in6KAtM+q4w2Xn/CUJ46urzZJFH48zCwcC6qrrtJonFOaD+qmNPqE99r/DcRfsqC/cW4Jrl3SwUgJySzSWlaU7wUrcXDitu2KD+niorFxfeF/V+1pNQG0SUcK6iooCXdih1nMFwbjqA4CtxbetqFlhPQoOd7dnh9XHxEMn9jidkV8ntI7+xrG2CW+dpIEHfvY3eeo3/yctTYks+c0Nsc9XHnmj3P7yNXLAZ919LyepTTKCuBRxX8ee2kC4LdX5Zkkum/qzxrQGnU/81KUG+95IZLNESoZLxqY1Cb+T6Y67NRBU9vItEpK90q3ilduNGkjhaLcbz+bSpj0W6HYWaC6B2bNnGwsuABaSqGQBrJJ7l7hmACvxwPvvv78phos2sc0IIBlxknoBiIlVwQXaWnRJsQTBGC7N3/3ud02d5H+0+4Mf/MC4O8MIDfimXVyXyf9bVlYWB+rO89FOskuzPW7XlLFx2ayd+zk20KSpWmMl29+Re9x9XpJJflu/Ym2P6w6mClnDirsdTlfgFzVyPYWGFHXbzmAusGaJWso1t++uSKuCurVLEl+qd6W9gVi3YZta0c3N2Vnv1Z1cAXGr0XXnL8MNOztOMHbW4mDen6FgdtjkUV2r1KEAVB+JRGXSTO/l2KGW+OamVVvlsVuf6QB+4wXaNn516X3Geyt5v1s/k2u+3dOo91qAa4K23Cwt4YUSnqAeBnpv91RaNTwxPGmKNEfe72lVr/xu0sCuvTXspk4Ntma7YoF+4IEHjCXzrrvuMnGmxMN2xwKNW66TBRpLJazGToGgCcZmWKDXrl1rWKCJIU4Ga7aOkwUa6yrkTvQDFmhcgG1KoGQW6HT6Ys9h1wBNrLa4MqcSACcW2Mcff9zE2QJciXdG2I/VGIHZGqIq3JUhCYON2YJjCKWsOzGuy4899piJ+zUVU/yjLc4BgRmxzpwDF2jimIm7hg3axhpv3bpVfv/73xvSK/TeFQv0woULZcuWLXLrrbca3aP/ZIt3iu70211RdTtvrqgy+Va7fFfuZgQ8R2uWLu+m1OA+XLNaJ2t0iCy9lYzcLKlcsqq31QdFvZ0rdsiWHQVKJNY5KOtqoFh/N+0olB3Lt3ZVbNAfq1mywsT+9fa+NgBOddm4vcJMzAx6haUxwHWLNqlOuy9ode7LzJIl85Ql1pMOGpj35Ls6sd39PV5bUS8r57t7ctWpvKq1WzR8wae8+L192qhBwfAA+KR6nbt/I8OR5dKqMdXh8r3UFTp9ENyqxpTWbGWKHjlWopENzq/H296DGvAAcB8rH6ulXUi/g6tvMgs0p8Ry0xsWaAsEO2OBtsPBytgZC7QtY9f0JRUL9PXXX29AJIA4FQt0un2x57FrCKS6E9IxQT4FQZQFvMl1AOgwSsOqTDnAPlbhk046SS677LJ4cUCyM+1Q/IBjY6+99jJWXsYECRgTCOiF3MGQiSGpWKDRTTos0FiSO2OBJoUTEwssgOv+LM3bdigLdEgibRY3++KWbp8pH1b3yVZ13W1YvzHdaoOyXO2a9gdhT0GwLa8RmdKw2d2ski3Kcrp8/XBNzZWJR3iPBAbjiBK+rNT6LXg2uFiad1ZIOBowpGG9ua/1kaPWjYD49fe2ebuXMoVLqb6qQSdmlKNNnQvQabJe7T6u2wbNrgL7c+XmWEo8F1+KKYe+btFGaW7s3ksDFu1Nq5St1xOjgbrNO5T8z6/M+DZ2tSc/krE6AeqqBbNW23KztLbGuCYAwOExZZrit/sJmai6PUcLiqXp8M/irqjTEM36O5DGrJibFf0pjd2LAe4jRZOPllQ9MC5bAUyVlpbKySefLLD7WiBH7lkA0fe+9z1TFEsj8adLliwRCJwggUrFAo0rMMRPCHGwpEvinDZFEPv33ntvIZ0SgoU1mQWaNubNm2eO84++ADZJ/QOBl2WBxloMIzLxtFacLNDd9aUrFmjbHmvyC3/yySfOXSZG96abbjKpgWCjRoitfu+990x6ImfhQw45xABlLK3E9kJ0heWY3LuwQCfLiBGx+DTO6xTqAD6xFPN9oGeYnwHGVgDSCHmVYW3G3Rod8z1jQbbWdcpRn+8Riz5pnogbfuqpp0x9W8580H8A9qlTp5qP9957r93dL9cZOdnSaq5Bn7QoCA4qoy4vcWk8B0y5COC3LUdroCC/X47x0+pUZkFewql4Len+caov0c5aahEJ5rWzfDoPuWXbp6y5uOW+v2S8HLn/MsEQzMted9IS9ku9guYPl45XnaqFJNPdj8MMJbdDALEhn+bxRStpXJAGxGm9BkOGpYzb+vsQUC8eT/Q6DMasROrZbACuXqp6bbb/XpoJGD1mJ26IAc7Oz/JUl0ID2QXp/c75NEFrVm7sWk7RjOt2meeD3qTcy7A+t+gPZOzXsbubmye1xv3b31K9WDNd/qwRX/u9GZ46XVrzCyW47GO9gSPiM+9F7ZeXsRDrxENk9DgJT96n/abXIj5f+tbj9ha9rb7WgLuf+H2sTdId2Xy8NA0Q6izeEzAMiEwWABUutaQsKldXZ+TZZ58161QpcnD9xU13yJDU9P8As7lz5wrA6jOfSU2Wg3UUdmTAM/HKCxYsSLBcm5O3/Uu3LxBO4U7slL/85S9iWaCd+5O3nSzQFqzjxo3AAo1LMy7gVtCxBbSAUwitAJvfSGKBtuWT1+vXr5d77rnHTFQwCWEFCzCTCfQBAA6IRQC1MFc72ZvZx/ePUA5AjjhjnCmD2HLmg/5jgsRKqjRa9lh/WAfy9MU2/lYcA8GklbHPyPihts7ycoywwmoM0EBgks4qHWa23fovd+wIgcW51RFXZWFb8quJ3Z+sK2bls4amvveTyw7Wz6EidX+urTcg+LX5e8nE0VuktKRa3SWjBmwkjxuXZ2J+N6vb88oNw7VeTNshTafiZgkNK5H61Uye+jT/Z1AC/rB5YUYnyfc1++y9HVYCvBaNDaYe4tcJQe5vT0RGTRkpy99aEVcFQJilMyH90bjpYzs77Or902dNkef/+KrUVnTtqRHR39PJB5W5WlfOwecMLTIeV3ZfMKNVJ1xa1dujawfQgP5+Oj3O8dqiLTeL31ciTtst4DYyYrT4t2+RjC0bxddYLz6dKGgNZUtkmGYnGKYGl1A7aEZ3Pl/ixLeb9bmnx+4B4D78BgBi3bnadnc63GABwPPnzzcAePny5cYyjEvuYYcdllAdUHj33XcbgHz++ecnHLMfrCs0+XRvvvnmBNBmy8BuDPhFcE+GAAoLJ3HAWIVtmqKe9AW34OQYXwsg7Xk7W3fFAn3NNdfEWaAXLVokEFwdddRRCU3B5EzcsmWBTjiY4gMAmBjnp59+2izOInPmzBEWYqGdpFrOMuQzxuJrATHjZFKCfdZNnPL0Ff0WFXX+EAF0Yz1nEqK/ytZxI6Xqg+T+AW1js8xO8AZwA2BY4GvHBDjJLc6Xin48TtvX3bWuGlEg631NEm6O5bDtzXmC9a0yUdvY4GI91u0zRtasJJ48Nk2wemWuBNeGZIim72HJVCZt2ItxdW4OB2RnVa7srMmTljCgLUbYhNVovLbTn++73lwfPamzY0q5rH3rbU1z6XQzxQKkEwkaA0j6o7joZlgntGB/T763i/adLB+1TQDGy7t0Y9zxY2ThRx9Jo6bsSUdaAxGpC9W6+jrsTE+hUZpPXlOY1cQoyVMW431nn6PGy9pNq3VJWcSVO7dnh6VmR0fXeiaxyJFu72ye3cT7mgmvpIma/JwsWblNw3ZYXCot4RxpaOKZkXw/64Sfv1xzGzoUg7d0JZM1iRM2wcAEyQ4lvz856vWDzTGaEq8zo1o/6F6fdcEDwH2myr5piBRBSHm53kwqWFz5UQfg4tbrFKydxKuSl/a8884zzMfO43Ybt1/SA/3iF78QwKUlh7LHk9dYOyGRIoaZ+GVree5JX7D0pmPtTT43n2GBBjw6BddkJgaw7GZlxWbUYJMm3y/g1OqLOli8GaMFrM52Um3j7g3rdLKcdtppcuaZZxrdY7VlIgBw/corrxh92/JY4WGdxh0bwdKOtZvvkhhiBLdqyh166KHm+zQ7U/zjfEw+WNbrFEX6/S6+HxaAfJdyykldHnb7QTxIIIRDj0yMdCoHHNDpIe+AupzqfYsnBh4YyeEHCfr59Rsiv74pYZf3oV0DPIeYvCPUhAnDTmXlhyJ/faDTw94BkeLiYkPwCPlkgigFxN9mPJqwy/vQuQYgxeRarK9vS3WmP5NvPvOs3PXMLZ1X8o500ID1SkuVIjJeeLvIdfv/Nf7R2+ioATw7MXpYb7+OJdiDDn+U+lA/2QvJq5NHp590q8+74QHgPldpeg3yQwOos0Kc6OLFiw1IAoDuu+++CczLyeCXeoCuU045RR588EHDAt2ZizNlrSs0oKw7AEz5Sy+9VN5++20DLrFK42L8wgsvGKKmXe0L7XclvPQni7Ws8yJrraqMCVAOCzT95Uf8iSeeMPHMfE5nnJyH9niQJgsvfJzXeQzXdAjD+IE4/fTTBas45FsAVwvCiVsmJhwg/7Of/cxMXNx///3mhYeJiK6E4wcMcEDDd8LkBBMznvReAzCM33fffWYyxb6g9L4199YkJALWebxZbEo692qj9yOvqqoyqeT4TWMi0JPea+Dyyy83RIvwP3jSew3grYb3GgYAT3qvgTvvvNNMzH//+9/vfSNeTXnooYeMx+aNN944oLUxefLkAd3/dDvvAeB0NdXH5ZghItbVCqAPYImrMiALqwUWQ8p19ZCEDRoATOxxVwCY8/zHf/yHad+es6s14BEWaKy/MB0TT9uXfenq3OkesyzQWLaxmiKkKwJEWubmdNtKtxwW3auvvlp++9vfmh87XJpnzZoVt5LbdnA3v+GGG+Rb3/qW2QUgvu666zpY8W15u2ZSg/YGsnz88cfm+h3o49jT34G1oOPp0ZXb/J7uZ38/v508YGJxn32UjMSTXmmAEA4EwOHd271SYbwSk6sTJkzw9BjXSO82sLjh6eVdj73Tn63FOyTXpKdHq5HerSGNhTfG02Pv9Pdp1/IAcB9pnHRH6QrszekITM9OhudUdfjxd5Yh32xngis0VlyndNUXLJrJLNDOusnbyX1JPp7qM9aEdOTrX/+6sCSLZYGGwRkrOn2wMcvJZflMbINTX6nK2H24UqcSLM+kNHKyQCeXIw44mQU6uYz32dOApwFPA54GPA14GvA04GnA04CngU9XAz6NLesiuOzT7Yx3Nk8DngYGvgbIy8ziTCE18Ef16Y8AjwsmWXBH6mpS59Pv2cA6Y0NDg6xZs0bKysri/AEDawT9o7fwMhDyMXr0aEPm1z96NTB7QbYBPL7cQDSzO7+hlStXGp4E0k160nsN2NSZlsiz9y25u+aWLVukrq7OeHe4WxMDY/QeAB4Y35PXS08DngY8DXga8DTgacDTgKcBTwOeBjwNeBrYRQ10nQhsFxv3qnsa8DTgacDTgKcBTwOeBjwNeBrwNOBpwNOAp4H+ogEPAPeXb8Lrh6cBTwOeBjwNeBrwNOBpwNOApwFPA54GPA3sVg14AHi3qtdr3NOApwFPA54GPA14GvA04GnA04CnAU8Dngb6iwY8ANxfvgmvH54GPA14GvA04GnA04CnAU8DngY8DQwoDbzyyity9913D6g+u72zHgB2+xXgjd/TQB9oAIZYmE092XUNLFu2zOT2Jr/1E088IfX19bveqAtbgP15/fr1hrk4Eom4UAN9M+QVK1bI97//famoqOibBl3aCgzapOrzZNc1QNrDVatWic1Nvestuq8F777uu+8c8Hv99dfL3//+d1m7dm3fNey1tFs14LFA71b1eo17GnCHBh588EH505/+JD//+c/l0EMPdceg+3iUgLT7779f/vznP4szO93IkSONXidOnNjHZxy8zc2ePVt++9vfSlVVlRkkKZB+8IMfyIwZMwbvoHfTyG6//Xb55z//aVJ73HXXXVJUVLSbzjR4mwX4kjt+v/32k1tuuUUyMzMH72B348gAvnfeeaf8+9//NmchPdyXv/xlueyyyyQ7O3s3nnnwNe3d133znVrwy7V46623ysEHH9w3DXut7HYNeBbg3a5i7wSeBga/BgKBgLFu/OQnP5G333578A94N4zwnnvuMZMIRx99tPzP//yP/OY3v5FZs2bJpk2b5MYbbxSs7J50r4Ff/epXBmTk5OTIBRdcIJMmTZLVq1fLNddcI5WVld034JVI0ACTBwg5V7EEezo06ujRPwAvE1xvvfWWuQ49S3CP1GcK483B/Txv3jw57rjj5LTTTpNgMCh/+9vfzO9mz1t0dw3vvt71798Dv7uuwz3ZggeA96T2vXN7GhgkGigpKTEj4cXOA8E9/1I//PBDefTRR+Wkk04yYPfAAw+U/fff32wPHz5ccFd7/fXXe96wy2q8++678uSTT5qJg0ceeUQuvfRS+eMf/yjnn3++cSV/9tlnXaaRXR/uhAkTTCOlpaUeCN4FdQ4dOtTU9kBw75SI5byurk7+93//V2644Qb50Y9+JNzjBQUF8vjjj0s0Gu1dwy6t5d3Xu/bFe+B31/TXH2p7ALg/fAteHzwNDHANEG+JYLH0QHDPv8yXXnrJVLr44osTKuNWdcYZZ5h9ixcvTjjmfeiogTlz5pidWIrQnZWvfOUrZhNLHNenZ8W0mul+bV+Uv/rVr8phhx3mgeDuVZayBLH8Y8aMkfLycmMJvvbaa72Y4JSa6rhz+/bt8tFHH8nhhx8uU6ZMiRdgUgGPGe5rv98vNTU10tjYGD/ubXSuAe++7lw33R2prq6W//qv/zJeHT/96U8T3J43btxoJmjOPPNM+cIXviA//vGP5YMPPuiuSe/4HtCAB4D3gNK9U3oaGGwaWLRokRnS5ZdfLldeeaUHgnv4BePeh+Tn53eoOXPmTLPPI9fooJoOOyxRE9ZKp+ACjTz//PNyyimnmBcTAN0bb7xh9nv/OtdAcXGxiftdt26dcS1PBYLRr0fW1rkON2/ebCZdpk6dKsRRA4LffPNN8UBw5zpzHrETViNGjHDuNttr1qwx1t8LL7xQPve5z5kFa7F3PXZQVcIO775OUEePPuB1cPbZZ5s6WIKtzJ8/X7gOmdCGoI3rlmcMoSMPPPCALeat+4kGPADcT74IrxueBgaCBjpzMwMAQ0IyatQowdrmgeCuv81kPY4dO9ZUsBZMZ21iWZHkOs4ybt2GLMxJGDZ69GijCoCuFY4TX41AigWAQ98bNmwws/MAEbdLsh6T9YG1CDd8LG2ACycIxu38u9/9rtGl87tIbsMNnzvTo50gJB4d4OGB4K6vhmQ9MqGFR8err74q1tuIFl577TVZsGCBNDU1GVdoCIh8Pp9Agnf11Vd7v5mqo66eG9593fV16DyarMeLLrpIIKYE7HJdIr/73e8kFArJTTfdJC+88IIQcgNBG94Jf/jDH+LlnO1623tOAx4A3nO6987saWBAaYAXYFxLSeeRLAAK3J8hw0I8EJysofbPqfT4pS99ybzgpQJjFlTwYPWkXQPo5Ze//KWQLsrqCD0Sjz5+/Ph4QSxEpJY666yzTMwgAO4vf/mLWHfz2267zbxAxyu4bCOVHpNVYF+U2Z8MgpnswvUU3QM+3Cpd6XHYsGHmxfiYY44x6vFAcOdXSSo94hkDPwLcCPZepwXAB27Q9957ryEOvOOOO0wKOSbCcJmGD8DNkupZ49SHd187tdH5dio9QsCG+zPvPDyD/u///k8+/vhj4xpt3fKxFMM/AQEjArmlJ/1HAxlKJnBD/+mO1xNPA54G+qsGSIWChXLu3Lkm1dGQIf+/vfMAk6M42nArIkBkRJBAJoqcBCKKJHLOmJwMJoPJBhNtMMZEG2MDIhgRjQGbYMBkMDkbMEmAyBmMkAAhJM1fX8k9/+ze7t3u3ezd3u7belYz09PT4Z3Z26mu6qpZ067qD75mQ2eaaaY0b7HFFnPTSXnt1DU6jhq6tFAT7pTiqBdimUfusssuPlucxSIv0DfffLOHUFl11VWzp/zFTz/ExSa/BYUa9ODLL7/0UEdaX6V9rQ/UC4fCokQPpxq6wvYMGTIkbLPNNum6YAlqepmWyZpebjR5E50UNSiussMqxbFYkJU5n777m2yySejfv79z1HrW2267zeuVaeq+++4b+vXrV7adRj/RGkc5shs+fHjBxIwsZiQQa9LrpZdeCq+//rofZ9euNzqzUuMrx1Hh9eT9WX/vYpKjQGl99bczJv0NkKZdWmA9x+utt1481XTbUr81WQh8r7M0yu+X46h3IAnAskSQFljP409+8pMWFendSBMymojVd16/96SuJ4AA3PX3gB5AoFsQWHbZZV1TJpPHYiFYJj5Z4TcOCCE4kvj/bTmOEijEsThp7a9e5oYNG1YQx1Zhkq655pog88otttii6bRvMg1fbbXVgtZgZYXgaIWQ5Th48OCSfPSyredZbLUusxlTOY5ZIVghuCTsDh06NIilrECOOOKI8MMPP4SFF144SMsu78Zrr7120wrBbXEs9dKLENzyG1eOY6nvtfhpcqE4KU8eopWiA7ziMs1wXO63Jo6d73Uk0fq2NY5LLLGEh37UZIIctGmSplTS+WeeecbPl1rLXuoa8mpLAAG4tnypHQINRUCCgtZbRSFYawGzmuBSgy0WghdffPGm1wRXw3HMmDHhnnvuCWussUZYcsklHbGE37/85S+u+VXcW2k9mjFp0qWUEJwV3lrjIusEzczLHFqa4mZNbXGU1veqq67ySYLpp58+/OxnP3Oz5xNOOCEceOCB4bXXXnMtpoRgORkrJaw0A9u2OJZigBDckkp7OGZrkSfoK664wq08NCnTzKm13xq+15U/GeU4atJaArL+BmpSsNxvsX5rZOkhJ1mlnF1W3hNK5kUAATgvktQDgSYhkP0hkFlktUKwNJZaL1hK29kkCH2YlXJ86623wn333RdGjBjhprxZ4ff8888Pc889dzNhazHW9r4sK5TFmWee6RM4MlurVGhu0YEGyWiNo8xOZYUgLYZirmrNr4Tfdddd182hZdanF0A90/o0c2qNYzkuxUKwTPhlNtnMqT0cIy9pf6Vt05ISmUM3eyr3W8P3uronoxxHPaubbrppWeFXvzXyNSErr5122qnp332qo1670gjAtWNLzRBoOALPP/98OO2009zZg16CpQ2uRgiWsCZHWuVmSRsOWJkBVcNR64Zk5qs1cHL6EjW/CL/BPcJefPHFYdSoUe7hWSa5EsRk2qw1wVGo1bN6yCGHhG+++ca1k+IoZ1gKm3TGGWeUNKMsc+saMlueddviKIFCpuZiHIXfCEPrVmX6t/zyy8esptxWwrEcmCgE62+k1lo3c6qG4/XXXx+uvPJKX5uuCZoLLrjAnV/JX4LC8jV7auu3hu91ZU9IWxxjLXonOuaYY4JiV+vvopxj6bdGEQg04dqsviYin3raIgDX092gLxCoAwL6QZQ5aNbZiLoloUF/2OWNWLOdcj4iwULhZCoVgrVesFnMf/LiqJc6/YjKSc5TTz3lZs/NJPyW4zh58uSwzz77BJmWyYmVnN3IHF8xlWVqlhWCFY7i1ltvdX7aPvnkk/5yctJJJ/lzXAdfu5p3oaMcpZXURMzRRx/tmt/iDje7RUc1z2Mxu3gsIVhLRpo5VcNRgvLpp58eXn75Zf8bKSsFrUeXRYI878pjeTOnSn6z5QyQ73XrT0klHONSsNtvv92tZLRMTH4TtNW7lPwNy1SaVEcEzKU8CQIQgIATMG/DiQkTia0rLSBis5rJtttum1jsu2TixIkF52ytlV9jWovEnOMUnGvWgzw5jh8/3vmaF9nEPBknH374YdNgLcdRAHROTMw8vIDHe++9l5jpo5+zGffE4jf6eXtJTi677LJEebr2iy++KLiukQ/y4mga9EbG1KGxVfs8dqixBr64Wo623te/z+edd15i8b4TEzgamE7lQ6vmN5vvdXmu1XCMtVh0gcRiAidmLZfceOONTfVbExl0h61iqpEgAAEIJKbJTWydaWLaXd/PIrGZdRcobEYzm53ux5cWhOCkJhwPPfTQphN+W3se9eBtt912iXl4TZ/B7I4mDXbccccWQnC2TLPsw7Fz7jTPYz6c4ZgPR36z4ZgPgcatpWXMjTrSTtMVCECg8wjcddddwbS7YfPNNw8DBw4saFhrJZXKmTnqGnnb1DoXE9Y8tmpBBU10UAuOMj1vJrNnPS6tcdR5PZPlnkd5KtZadSWbnAlnn322Jnv9uNn+g2Pn3HGex3w4wzE/jqqp3N9IfrMr48y7T2WcumMpBODueNfoMwRqQGDOOef0WhVyR44c5K1Zgte3336betLUWphyabPNNvNTUQiOPxzlyjdqfi04yjFOs3l7bo2jnh15d/344499/V+pZ0lxfWPYKAnBl156aaliDZ8Hx865xTyP+XCGY34cVRO/2R3jGb2Iw7FjHOvxagTgerwr9AkCXUBAToTkpMHWmIbDDjssHH744b4vYViOMoYMGRIU5/OWW24p2Ttp3ZQOPvjgYGtVwyyzzFKyXKNnwjGfO9waR7Wwww47eEPS9Mq7c6mkZ3KDDTbwGMrN6lkXjqWejI7nKdZsNvE8Zmm0fx+O7WeXvZLf7CyN9u/Dsf3s6v1KvEDX+x2ifxDoJAIylTKnQuGxxx7zUDLmPChcdNFFYcCAAW5GtfTSSwd509X5ueaaK9UKx+4pPqi8cUogkYfoZk1wzOfOt8ZRLcgrsSZrnn322fDCCy8EhT2RF92YFIbCnOL4syjPxc3ifTyOP27hGEnkt5V1zL777uvP4LzzzusV8zxWz1fxUeWxXd/b+N2FYz4c9b3nN7s6lnrnefPNN92ySF6dxRCO1THsTqURgLvT3aKvEKgxgbfeestjKCrWp34M3n777bCWhZTo3bu3a3T1o/Doo4962IR333039OvXz8PNSPhVPEbzFO2CSI27WffVwzGfW9QaR7UwdOhQt0qQQKJQUUp6YdFLtWIvSjOscCjROsELNOF/cMz3pn/00Uce4kRmkbKMiUIwz2PlnGVJJEsjTaped911YdKkSW6BpO8vHPPhKCssfrMrY6kJ06OOOsrjoSt8kb7b0v6KIRwrY9jdSvWQf6/u1mn6CwEI1IaAtGnmvj8cf/zx4ZRTTgmjR48Ow4YN81iLiv+rpDwJFxbyKO2EAr7LxPSggw5KZ/LTk024A8d8bnolHPXiPGrUKP8ohmhMEkp+9rOfhRVXXDFmNe0WjvncesXhvuCCC4KF2nKHgapVMT5l9bLKKqt4IzyPbbN+8cUXwwEHHOBWGYsssohPWMmkXJOtis2tCVc45sNRtfCb3TZLLfnS93vxxRd3vyea/JdVwllnneWadDi2zbC7lUAA7m53jP5CoMYENCfWo0ePIPM0CRClhGC9nDz55JOu/ZXTK4sdHAYPHlzjnnWv6uGYz/2qhKNakkXC66+/Hr766qvQv3//sO666/qLdD696P61wLFj9/CBBx4IJ5xwgps9b7HFFqFv377hkUcecYuYYiFYLfE8luctPxFykvjHP/4xzDjjjGHMmDHh5z//uS9pyArBcCzPUGeq4chvdnmWzz33XDjkkEPCEUccEbbccku3frvkkkvClVde2UIIhmN5jt3tDAJwd7tj9BcCnUigNSG4E7vR7ZuCYz63EI5wzIdAdbVoScguu+ziL8MjR450rW+sQWGmzjjjDA+zldUEx/NsWxLYaaedwp577hnkoC0mmaBKCJF2vVgIjmXYFhKAYyGP9h4p8oWiBFx77bUFVcg0XxYfxZrggkIcdFsCrAHutreOjkOg9gRk9qz4vjINktma1lrq5UQmaqTKCcCxclatlYRja3QqPwfHylmp5P333+9rfn/605+GxRZbrODiBRdc0OOmq4y0xNk1wQUFm/zg/fff92UKstKQSb5+R7JWQ9NNN11Yc801XaMup3bvvPOOe2/XmmDS/xOA4/+z6MiefJxoHbq+s3omx48fH2TZkU0Ko6fnUn5P9P1eZpllQgwrly3HfvckgADcPe8bvYZApxHgZTkf1HCEYz4E8qmF57FyjnJ6JS3RiBEjggTeaBqevQAAQABJREFU4qQ8eY+VKa9eqBdddNEwzzzzFBdr2mMJGoopL8H2mWeeCVrvO3HixLDOOusUMEEILsDR4gCOLZC0K2PcuHG+Bl3OrjSxr++uTPIVSWD22WcvqBMhuABHQx0gADfU7WQwEKgNgeKXZc3cxwDxtWmxMWuFYz73FY5wzIdAZbVIYJPwIWd/sogpleSA7aGHHvL1g//617/c+Vrxy3Sp6xo975NPPnHhV78Z8imhtdMSOLRGWt9jherJpmIhWJML888/f7ZIU+7DMb/brrXnjz/+eNhtt93Chhtu6BpgRQyQpZsmufQMZlNWCJYVnDTF8pNC6t4EEIC79/2j9xDoNAJR6Bg4cKB7fO60hhusITjmc0PhCMd8CLRdixw1SbiVsxwJY6UEMsVA1/l99tnHY6XLQdbmm29esF647ZYar8SNN97oml/FlF9iiSXcrFnbBx980EOYyWGdjrMpCsFzzz132HjjjbOnmnYfjvndejlc23rrrV0LrCULm222mVtv6Dssc2eZ55cSgjWhtfvuu7vjtvx6Q01dRQABuKvI0y4EuiEBCR0y7yN1jAAcO8YvXg3HSKJjWzi2zk+aX4VHUcxaCbaaBFxggQXSi+QkS46wFC/0F7/4Rfjiiy/C888/72F+llpqqbRcM+1IU6ZweWInIXerrbZKhy/zcGnVtK5SAkc5IVjMmz3BMZ8nQGt+Tz/9dDd11nf40EMPDQMGDPDK5cVdQq9COyqGfDkhWCG7Zphhhnw6RC1dTgABuMtvAR2AAAQgAAEIQKCeCUj7069fP9fuSnv5tsUJldnzq6++Gn7zm9+4We+xxx7ra3+lMZawLPPdZo1Dfccdd7jjsNdee83NnrMCsO6ztLttCcH1/Dx0Vt/gmA/pL7/8Mlx88cXh7rvv9gpXWmklD2kWa5eztUqE4FiebfcngADc/e8hI4AABCAAAQhAoMYEJLBJKykvxjKXlCD88MMPB2mAjzrqKH+BVhd0LJNVmfbqRbsZkzzmSuv273//22NzawJBGrRsQgjO0ii9D8fSXKrNlUnz6quv7ksZtN5X2t6NNtqoYIkCQnC1VLt3eQTg7n3/6D0EIAABCEAAAp1EQOa722+/fVhhhRXCwgsv7GtUDzjggAJnTueee2546623wh577NHU3qCHDh2aCsFyMCRmc8wxR8GdygrBEpCXW265gvMchADHfJ4CmS9HIVie3WPM6axDq6wQ/Nlnn7mTLJnokxqPQI/EUuMNixFBAAIQgAAEIACB2hP49ttvg16cpfEcNWpUuPrqq8Pw4cN9zWHtW6//Fi655JJwxRVX+FpfTQ6U8iMhgUTCMKk8ATiWZ1PNmQ8//DAcfPDB4dNPPw0bbLBBOO644/z7m61j0qRJqeVCNp/9xiGAANw495KRQAACEIAABCDQiQRGjx4djj/++PD555+HaaedNowdOzYMGzYsnHrqqS08yXZit+quqUqEt7rrdB12CI753JRKhOB8WqKWeiWACXS93hn6BQEIQAACEIBAXROQB1mZO8vJzswzzxx23XXXNN5tXXe8kzsXzXhlCi3vzzKHJk5y9TcBjtUzK3VF1hz6hRdeCLJAkNVG1hy61HXkNQ4BNMCNcy8ZCQQgAAEIQAACEKhbAiNHjnQzca2rvPLKKxGC23mn4NhOcEWXZTXBO+20U9h///2LSnDYqAR6N+rAGBcEIAABCEAAAhCAQP0Q2Geffbwzig+MBrj99wWO7WeXvVIxvc8//3yPWb3llltmT7Hf4ATQADf4DWZ4EIAABCAAAQhAAAIQgAAEIDCVQE9AQAACEIAABCAAAQhAAAIQgAAEmoEAAnAz3GXGCAEIQAACEIAABCAAAQhAAAIBAZiHAAIQgAAEIAABCEAAAhCAAASaggACcFPcZgYJAQhAAAIQgAAEIAABCEAAAgjAPAMQgAAEIAABCEAAAhCAAAQg0BQEEICb4jYzSAhAAAIQgAAEIAABCEAAAhAgDjDPAAQgAAEIQAACbRJIkiQ89thj4eWXXw4ffvhhmHPOOcPSSy8dllpqqdC/f/82r6cABCAAAQhAoB4IIADXw12gDxCAAAQgAIE6JnDppZeG0047LYwZM6ZFL3v37h1OOeWU8POf/zz07FnfhmXjxo0LJ510UjjyyCPDwIEDW4yFDAhAAAIQaHwCPWxGN2n8YTJCCEAAAhCAAATaQ2CvvfYKl19+uQu3O+64Y1h55ZXDEkssET7//PPw3HPPhYsuuih8+eWXYe211w5333136NWrV3ua6ZRrNtpoo3DnnXeGd999N8w777yd0iaNQAACEIBAfRFAA1xf94PeQAACEIAABOqGwIUXXujC76BBg8K1114bVl999YK+bbfddmH//fcPm2++ebj//vvDjTfeGLbffvuCMvV0MGHChHrqDn2BAAQgAIEuIFDftkpdAIQmIQABCEAAAhAIYfz48eGoo45yFJdddlkL4Tcykib1nHPO8cPf/va3MbvFVvU9+uij4amnngrfffddi/MySPvqq6/Sc1OmTAkvvfRSePjhh70vLS7IZPzwww++Nvmee+4J7733XubM1F3VpbonTZrkGV9//bUfx3wdl0s6N3bsWD+tfqueaDz3zjvvhEceeSSo/bZSW+Nv63rOQwACEIBAPgQQgPPhSC0QgAAEIACBhiJw8803u+A5fPjwsP7667c6Npk/X3HFFa4lLi4o8+gf//jHYcYZZwyrrbZaWHHFFcMMM8wQDj/88PD999+nxT/99NMwyyyzeP4NN9wQZp11VnewJa3zTDPNFI4++uhUgI0XSYD9wx/+EOaaay43y15vvfXC4MGDw0ILLRQefPDBWMyFYtUtYVppySWX9LYmT54cFl544TDbbLOFzz77LC0fd95//30vt8kmm3iW+qx6nnjiibDsssuG+eabL4iPrtcYtca4OFU6/uLrOIYABCAAgdoQQACuDVdqhQAEIAABCHRrArfffrv3f/nll69oHLvttpsLk9nC33zzTdD1119/fdhpp53C3/72t3DdddcFrcU999xzw2abbZYt7vv//Oc/ww477OAepi+44IJw/PHHu8B85plnBplkZ9Ppp58eDj744DD99NOH3//+9+G2224LJ598cvjoo498TbKEeCUJqBdffHEYMmSIH59xxhl+3KdPn7Dzzju7YK1+FaerrroqSMjeY489Ck5tuummnn/TTTe5oL3VVlv5GHffffdUO6wL2jP+goY4gAAEIACB/AnICRYJAhCAAAQgAAEIZAmYZlNOMpM///nP2eyq9o877jivw4TjgutMqEy23HJLP/fXv/7Vz3388cd+rDZN0C0ob4Kmn1t11VXT/BdffDExATaxcEyJaW/TfO1YuKakR48eiZlnJ2Z6nJ5ba621vB5zgpXmPf/8855nmuk0L+4svvjiybTTTpuYCbRn7bfffl5W9ZppdCzmWxPw/ZwJ2ml+NeNPL2IHAhCAAARqSgANcP5zCtQIAQhAAAIQ6PYEvvjiCx/D3HPP3WIsr7zyinuDlkfolVZaKf3IvPnpp59Oy0vrqiQtbTaZcBr23HNPzxo1alT2lGtz99lnn4K8NdZYw49lJh3TLbfc4mtvFdJo9tlnj9m+Vb+23nprN31+4IEHCs4VHyyzzDJhueWWC08++WQYPXp0evqZZ57xdcXS7sp8O5vUpsy4s0lhoJTuu+++NLs9408vZgcCEIAABGpCAC/QNcFKpRCAAAQgAIHuTcA0q0GCrmlIW6wBlkMnrYMtlaJDKTmLUqgk06CGSy65xD/Z8nH9b1bo1Hmt4S2OJywTZsUbzjrPUt+Uyplor7DCCu6V+rXXXgtxDa9fUOI/CeMK6SSTZ8U0VoqCebH5s86p7uK06KKLer/FS6m94y+ul2MIQAACEMiXAAJwvjypDQIQgAAEINAQBBZYYIEg7am8NhenYcOGBTmQyqaNN944aP1uTIq1qzRx4sRwzTXXxOyCrbSochKVTXJ4VSpJa2w2cemp6O1ZgnqpNMccc3i21gO3lbQ+WVrdq6++2gVgeYtW2Kd55pknrLPOOi0ul9Ot4qT1xNIUx3HHbbXjL66XYwhAAAIQyJcAJtD58qQ2CEAAAhCAQEMQkAmxkjS9MXxQdmDS0mY/ElCzaeDAgX642GKLBWmFy30++eST7GUV70s4VSon4H744Yd+Xt6k20rSMCuW8Ztvvumm0HfddZd7hd51111baKNVVymP0TFEkjxMK9V6/N4I/0EAAhCAQNUEEICrRsYFEIAABCAAgcYnsMEGG4T555/f19HKK3MpIThLoTgWrtblKmTQq6++GuJ64mx5aUhlbvz3v/89m13xvsIXKZUzxX788cf9vATwSlJckyxP1dF7dCnzZ9UVzZyz9UZNeTTJrvX4s22zDwEIQAAClRNAAK6cFSUhAAEIQAACTUNAa261JrZXr16+llZxbj/44IMW49daXIUzuvfee/2cyse09957u+B8xBFHFJgv67zOnWwhi4pNoOO1bW2lnZ1mmmnCOeec06JfckT1j3/8IwwYMCBIkI9J5ZW0Prc4qZwcfkkAvvXWW8Mqq6yShk0qLquQTNmYvxL+NRYlhUiKqZbjj22whQAEIACB6giwBrg6XpSGAAQgAAEINA0BCzvkwu9ee+0VFPNWn/nmmy8oXwLgs88+mwqf0hafffbZYc0110z5nHDCCeGGG24IV1xxha+NlSZZzq9Uj9YXjxgxIqju9iStUVaMYLUxdOjQcOihhwblyQv1+eef79pnmTLLCVdM0SxZa36XXXZZjwUcz0twVyxjxQhWigJtvDa7ffvtt93z9UEHHRT69u0bLr/88vDoo4+Gk046KWgtdEy1HH9sgy0EIAABCFRJoKZBlqgcAhCAAAQgAIFuT+D9999PjjrqqMQ0pB7r1l41fKsYuYqf++tf/zqZMGFCyXGatjUx8+LEtK/ptYrRu+222ybmyCq9JsYBthBGaV52RzF/TYDNZvn+ddddlwwaNCitu3///smGG26YmCDcouxbb72VmEl0WtY8PxeUMW22n+vXr1+ifhenGAf4oosuStTPyGHBBRdMTjzxxOLiflzp+EteTCYEIAABCOROoIdqrFJmpjgEIAABCEAAAk1IQK8MJqgGE4g9Du6QIUNKOokqhUZeo9944w0PZSRNbXFs3VLXVJMnZ1pyiCUnVDLfbi3J7FpOu7RGOZvkBGuhhRYK0hDLI3Rx2n///cOFF14Y7rzzTjetVlzisWPHhrgeubh89rjW48+2xT4EIAABCJQn0PovRPnrOAMBCEAAAhCAQJMRkNCodbL6VJtkYrzIIotUe1nF5RUOqVxIpOJKynmGHjlypBfV2t1KkkItxXBLbZWv9fjbap/zEIAABCAwlQACME8CBCAAAQhAAAJNS+Dll192T9LyVq01zIpxvPbaazctDwYOAQhAoNEJIAA3+h1mfBCAAAQgAAEIlCWgeMHREZe8Rl9//fVly3ICAhCAAAS6PwHWAHf/e8gIIAABCEAAAhBoJwF5s5bQa46vfF2v4veWS1rDHNcZF68fLncN+RCAAAQgUF8EEIDr637QGwhAAAIQgAAEIAABCEAAAhCoEYGeNaqXaiEAAQhAAAIQgAAEIAABCEAAAnVFAAG4rm4HnYEABCAAAQhAAAIQgAAEIACBWhFAAK4VWeqFAAQgAAEIQAACEIAABCAAgboigABcV7eDzkAAAhCAAAQgAAEIQAACEIBArQggANeKLPVCAAIQgAAEIAABCEAAAhCAQF0RQACuq9tBZyAAAQhAAAIQgAAEIAABCECgVgQQgGtFlnohAAEIQAACEIAABCAAAQhAoK4IIADX1e2gMxCAAAQgAAEIQAACEIAABCBQKwIIwLUiS70QgAAEIAABCEAAAhCAAAQgUFcEEIDr6nbQGQhAAAIQgAAEIAABCEAAAhCoFQEE4FqRpV4IQAACEIAABCAAAQhAAAIQqCsCCMB1dTvoDAQgAAEIQAACEIAABCAAAQjUigACcK3IUi8EIAABCEAAAhCAAAQgAAEI1BUBBOC6uh10BgIQgAAEIAABCEAAAhCAAARqRQABuFZkqRcCEIAABCAAAQhAAAIQgAAE6ooAAnBd3Q46AwEIQAACEIAABCAAAQhAAAK1IoAAXCuy1AsBCEAAAhCAAAQgAAEIQAACdUUAAbiubgedgQAEIAABCEAAAhCAAAQgAIFaEUAArhVZ6oUABCAAAQhAAAIQgAAEIACBuiKAAFxXt4POQAACEIAABCAAAQhAAAIQgECtCCAA14os9UIAAhCAAAQgAAEIQAACEIBAXRHoXVe9oTMQgAAEIAABCEAAAhCAQIcJjB8/PnzyySfhiy++CHPPPXcYOHBg6NWrV4frpQIIdHcCCMDd/Q7SfwhAAAIQgAAEIAABCBiB77//Plx77bVh5MiR4cUXXwy9e/d2ofeHH34ISZKEtddeOxx55JFhtdVWCz169IAZBJqSQA/7MiRNOXIGDQEINC2Bjz/+2F8MigFoZnymmWYKs802W5hvvvmKT3P8PwKPP/54GDduXEU8VlxxRWdaUWEKtUlgypQp4d577w39+/cPq6yySpvlKQABCITw3XffhYkTJ4YZZpgh9OzZuKv/XnjhhbDNNtuETz/9NHz99ddlb/2MM84YVlpppXDTTTf535KyBTkBgQYlgADcoDeWYUEAAuUJXHPNNWHnnXcuX8DOLL744uGwww4Le++9d6vlGuGkhNmTTjrJtQIykWsrLbfccuH5559vq5iff+SRR8Kqq65aUVkKtU3g22+/DdNPP31Yaqmlgl52SRCAQGkC//nPf8IZZ5wR7rzzzvDNN9+4JlQl11prrXDssceGlVdeufSF3TRXfw+GDx9e8eRknz59grTCX375ZZhlllm66ajpNgTaRwAT6PZx4yoIQKABCCy66KJhyy23TEcigxiZjz333HPhwQcfDPvss0/QS8Luu++elmnEne23395fEiXwV5PERevKWkuDBg1q7TTnmpzAV199Fd55552w5JJLNszaRAkVb731Vvjvf/8bFl54YbcoKb7NmsiQNr84TTfddN1WQzlp0qQwYcKE4iH5fZ122mlb5Ms8d6655goDBgxoca6jGeecc0741a9+5cLg5MmTC6q75ZZbwn333Re23nrrcMUVVxSca8+B7qPW2pZLMkHWfVWqllG5OovzX3rppbDMMssUZ7d6rOdU2nCZQj/11FM+sdbqBRWelOZZ/Wlt4rOSe19JmQq7RDEItCQgE2gSBCAAgWYicPXVV2vpR7LddtuVHbZpDryMzYwn9oJTtlwjnDCNiI/13XffrWg4yy67rJd/8sknKypPofwImCbL2ZsGOL9Ku6Cm9957L9lwww0Tm2Dy8ZhJd/KXv/ylC3qSb5M333xzMs888/iY9DfG1lgme+yxR2ICUtqQ/p6YCWpaRuXi5+GHH07L5bFjAldy4YUXJma1kdjyjmTOOedMNt988+TRRx/No/qCOs4+++x0HHE82q677roF5X7/+98nP/rRj9KyOj927NiCMh050HM0zTTTpPVn+5LdV5ltt922I035tc8++2yrbek5j6lSRrF8JVsTZJMlllii1T5kx128b5MTyZlnnllJU22W0fO2wQYbJAsttFDJspXc+0rKlKycTAhUQQANsP0lIEEAAhAoJnDIIYeEU045xbU4r732WpC2OJvs72wYM2aMf4YMGRLmnXfe7Gnf17ozaZS1rlgaH5kNL7/88qFfv34tyspTp2a8VY+0RuXWqUnTIFM3aaalNSulWcm2Kycn0kSp7cGDB4cFFlggdXwizYVm66WVUNK+NHIzzzxzi/51NEOaIX20zlrr8IqT2lWKa/TshTjYC6qPT9c988wz3v9SnHUvsuVffvll5yOO2VTJPYvlxUT3/YMPPgj2sh4WXHDB1IQylonbzz77zMvqWKbzs846azzVYltNH3Sxyuve6X7bS26L+rprxn777eds9czPMccc4eSTTw477rijP//ddW3zK6+8En784x+75uvKK6/0vxk22RaOOuoof5ZNEPXbpb8b+q4deuihYemlly64hcXPbMHJdhzI4ZGen7hmX9+TW2+9NUgLev311webBGxHraUvUTvynXDCCScUFMhagTzxxBO+tOSss85yCxtdoz7ssMMO4fbbby+4rj0H//rXv/weVHKt/jbffffd4Y9//GM44IADKrmkZBn9fbj00ktbnLv//vvDVVdd5ZrmeLISRrFspdu///3v/l2qtHxxOf1enHrqqb4sqC2LnuJrs8d6pg888MDwz3/+M5gAnD3l+5Xc+0rKtKiYDAi0h4D9uJIgAAEINBWBSjTAAmJCr8+q20tVAR9zHJLYS3vBjLu9uCaPPfZYQbm99trLy9x1112pxsfWbyb2I5+WsxflxISmgrqk3SvW0FgYi8RMlV2jZH/rvbwJk4mZLScmIKb1aceECz9vApzPxksLFa/RzLwJk17+7bffTvPjeW3bSu3RAJvQ7loZMwdMzMS8oInzzz/f+2FOWRJzVJOYkzI/Fj9pTPr27Zv2U2yKNYXSXKvfBx98cLLLLrukZbMa/krvmTr2j3/8I7GX2rQe1W2TFslvfvObxMwp077bi2Oy0047JboPWX7m+CsxYSgtF3eq6YOuOe644xJ7IU3rtvXZyT333OPH3VkDbAKYj8EEnojGrSzE3AShNC/PHX1/pO1bZJFFEjP5TGzCJc/qvS4T/Hxc//73vwvqlgbQJnZSS5Ibb7zRy7355psF5fI+0N8Lafeyz2Z2X99FE9Jya1bP5K677tpqfTYBmOj7kU0mPHofS31nsuUq2V9zzTXLjjc79uy+mWEn0qLmmWzSMdH3VX/DsqkSRtnylexvuummVY85O37t63dJlgLtTfq7JMsHWRnoO1ZKA1zJva+kTHv7yHUQyBJo+00nW5p9CEAAAg1AoBIB2LQ0iV4Q9XJgTkLSUV9wwQWep5cbCW7mYCWRubR++CUIPfDAA2nZKACb5tVNDzfZZJPENImJaR68jGlh/JpYl45/+tOfulmoaWGT+IIs80nTrHi75rwr+dvf/pZcd911SXzxWW+99dI2tRMFYL2EmEfr5KCDDkouuuiiROU0HglVEjRNK5RcfPHFiWmwPV/j0HFbqT0CsOq0dXnejswx4wunrRVz4VL8bN2kNx0FYPXdNOEuCGpC4M9//rNzlOlidhIhCsDirPJrrLFGoj6qPaVq7tnrr7/uQoNMRc0xWCJhTVwio/POO8/r1H8/+clPfDy2jjzRS/xf//pXf+EVY1vX6Ixj4Wr6oGvOPfdcr1vjsHWKiWnrko022iidDOjOArBZVyQyeZa5ZDZpAkPPQd5JE0S6J/H7rGdEExp6zvJMTz/9dKK/LcXJ1sr75I++c0oSlLW0QkkMiiew/EQH/7N11emkm8Ze7rP66qt3sKWpl2sM4hu/HzLVL06ahFA/NJGUTTFfz3xHksyoy5mWlxu/8s1io8XkZUf6oWs1ESDBWoJwTJUwimUr3WpCrj1jLsVj/fXXr7TZFuU08bjFFlsk+t3Ub1SxABzvcWv3vpIyLRomAwLtJIAA3E5wXAYBCHRfAlEAlkZILxDxI6Hs/fffdwEzamWl4YvJQkskEkz1Av3GG2/EbN9Kq6mXCnNEkr7YRwFYwquZhxWUV13Szujl5aOPPio4Z6aRXtcRRxzh+dIEqu7ddtutoJzWEkr40jkJXzFFAdjMnRMzvY7ZLnRqhl3lpZWOqb1rgMVo2LBhZT/qdzapv1rvp/Z//etf+4u/mYD6cVarGwVglSt+KTbzZp80MFPoVBsbBWCV1/qxbKr2nukFXvX84Q9/yFaTmJMYF2IsxEiaLyFGkwlRmI8nNDEx++yzJ3E9Z7V90KSAhAndv6wgIX5av6n+1VIAllCmNrLPVBxbHtuNN97YJ4KK6zr99NO93ewzW1ymPceayMhaEWhsWnu87777tqe6qq6RUGYOkBIzRU6v0/OhiTBNdmmiRH3RpI15LU7LdHRH3yf9rdJYW/uUmohoT9sS/tXOnnvumZgnYrdUkZWM7mmc6JDPAJWR1Utx0qTWMcccU5xd1bEsWyTMtjbeUuek/dQkU17pjjvu8D5cdtllBVVWwqjgggoOZMmge1hqXNXmaR1xe1NW0C8lAFdy7ysp097+cR0Eigk0bjA0++aTIAABCLRG4IYbbvA1qVqXqo/WWZoZV9hqq62C1pHKO6YJYGkVWjOntaqmifM1oekJ2zFNXVB4IDN/9E/2nAmuLda92suQx6aUp2l5Qs0mraG75JJLgs2qe7ZpZX1rGrJsMV/Lay+cnjdq1KiCczqwF+yCNcImVKWxY00oa1G+2gwxkvfQch95ws0mrUc2La6vkf3lL38ZbILA1zOLgTxRFyfTwrZYm7fYYosF0zQEc6IURo8eXXCJxqe1pdlU7T0zwdUv17NhgnVa1QorrOD3S/kxKV60OJo2Pl1HrXNaX6l1wXp+lKrtg9YxmtAQtA49eo9VPeJXvL5S+XknM4v1KvNcH5rto9YKil1ximunbQKk+FSHjk2z5DFgs5XYpEX4/PPPs1m57+seKtyatr/73e/S+rUO1JYfeOgZrR01Qdy/Bwphk9fY5StAY6wkVVqutbo0JiX5JzDLCP/7ZVpADzd0/PHH+zndd6Vy994mAv18e/9T/TY5WfXluj9aG51X0m+G/o7Y5GlBlZUwKrigggON2SZ3KijZdpG4Trztki1LtOU3opJ7X0mZli2TA4H2EcAJVvu4cRUEINAABBT7UC9pMUkINnNkd3ok4cW0ffGUb6PApR/qYkFLBeKLpMoNHTo0vbaUY5v4MmSavLRc3NELol4ilSRw60Vdzq4kFOuTTXLkohT7lj03//zzZw99PwrbcnzS0WQz9sE0wFVVI4c4Zo7tjm8Uj1mOnbLCQbYyOQgq9XKna2w9rTv3sfVm6SW2htQnMdIM24lcKr1npp10R1Zmyu7PgUKL2BrOYObrLtBKCI3JNPQuoJu5YzAz82DabZ8c2WyzzdyxUyxXbR80iaIkJ2fFSf1pz0t+cT2tHWscit9cbViV1urMnpNzMzMHzmb5vs3Q+1YCSZ5JE1ZyQFVcr+5TrZKc3smxk2kDgxxiZb/nev7N8sNjtqp99WOdddbxiTfzxhts3XuHu2VWJ+54S/FvW0uKKV3KKV9r15Q6N2LEiKDvs8ZiGkkvogkuTQzK4VV0BFbqWuXp3hffn3Jly+XLmVp8hsqVKZWvv62abMsjaWJDjrVMm+38s3VWwihOAmWva21fTqv0rOWR8mJQqi/6zpdL8d5XUqZcHeRDoFoCCMDVEqM8BCDQMAT0oi/tXKUpagTlkTgKsMXXyotxsRZHLynFydaaepZe2lpLsU0JDHrBLJXUpq1TbnHK1lO2yIsCXHteFFtU1s4MeaaVACChVMKsXkBLpSisF5+LGqTIJp4vxTmWqfSeaVJEwp+8EpsZaarRt3XArvVXnpmRe5OaBJHFgAQWaW3NuZF/NJEigVgv/tJKV9sHW0fn9UdtdByftrJSiOPP5ue931oMz462pUkmcxjXopr4DJd6blsUriJDkxqyhpC3X9Ut7+cS1GxtbhW1VF5UGme1KQ/XeiZksZBNOleclKdnTzHI80jSJrcl2OpZilYmHW1Tk22lJtykAT/66KM9Lqy+K0rxPmfbVF5H77s8sOv7Vm2yJTBBf5PySNLo629sqQnSShiZKXxV3dB49XdSgndHkvoc/651pJ5y1+o7r9Tava+kTLn6yYdAtQSqtxWptgXKQwACEGgQAvEHWubGEt7KfRTeJJui0JnNi8Kdwh+VSnpJV4ptyvS3XHvKL1dPqbq7Ok8mz+qzhH9NQFx77bUluyQz4lLpww8/9OxiDWkpzpFfNfdM5ny2FjioHQkk5rjFXw7NKVmw9dIezin2y9ZzBpkMS0sv82hpvSTQS6stoVmp2j7EkFvFEymqS8+FrAK6cxIPPa/FGj9bf+9CoK2LzX145oQsyGLhT3/6U7jtttuC+QHIvQ1VqGdGQowEEj0XxcKvnnvzmOsm/MUd0KRUucmg4rJtHcty4vLLLy8rEGqSRhYrv/rVr9qqqqLzEvbvu+++smU1rjhBpfucTfqea4JP4eQ6mmROnl020FZ9sqYwR2Ahr2fO1hK7xYisUYpTJYyKr6nkeOutty57nyu5XmU0+WL+JCotXnW5Su59JWWqbpgLIFCGAAJwGTBkQwACECgmEF/QpCEslfTCKcGn+AWvVNloFq01c8VJa2f1wiiTOWkB9XLy6quvBmmWipO0i4pXrFiQ3SGZY5hgXqyDhSsJ5iTKx6kYnFrTW5zMGVQLIUlltOZYqRKNRbX3zEIg+TpbCZ8SqGXCKXNGCU8yY9XaSmkvdV73Wi+8ShKaZTIvDZCeAyVzNObbavug9cZKcZx+8L//FGs2mtpn87vTviYNtN5QpqIxSbDXM7zBBhs495if51bm+oo1XK2WrdI+yBRVEyTaPv7448HCerW4VM+PeWP3WLjZk+ZN3ic28tS8m1dff1Zl5qznU8KezExlZisWig+el8BtHvHdjFtCXjZpckuaXWln1ZZMvbV8IZt0LIFc/e1oOvzwwwuWH7RVn567rJ+Htsq3dl5/n/V3rFwc60oYtVZ/uXPFvgLKlWstX78zei5rlSq595WUqVX/qLcJCdiMIwkCEIBAUxGIXqCzcWIrASBvzYrnqRAqxR5bH3roIfd8qnjAig+rFL1A33vvvS2qN42he2lVffI8nU0KB2M/R0n0Am3r5/xY4VTshS1bNA1tpDA8MZn5nZdXiKbidNppp/m5bLgjEzo8T7F6K0kKzaP+yWtnNUlhneSxVF5XY4inGBpJXnLljVvJhEuvX22Y2WpBEyY0+blsLFGbBPC8UiFdqr1nChmldhWqJ5tMW5mGQlJ7psF0b9R6Fj744INsUfdyqzoUJkmp2j4ohI0Y2UtpQage3Xt5LlfdtfQCXTCYGh0o5rOZhHq8a/GU92B5RC/+LtSo+ZpU+/Of/9zvjcai57r4Ez166x6aMJr89re/TTR2xRlXzHF5NjcNce59kydqm1zw9mzSxpnrec4zyQOzPG2bAO+xqvWdOPHEE51H1pN79JB87LHHJqYtT3RsJv2JCa65dUf16jtiQrVvtV/qo79FJrDn1q5+A9SOwtSVSpUyKnVtW3n6u9Veb9BmRp2YVURbTVR8vpQXaF1cyb2vpEzFHaEgBFohQBikVuBwCgIQaEwC7RWARUOxf/WSo5eNX/ziF4mty00OPPBAF1ZMY1gyDnApAVh1xdiwijmr+IgSYs3hkr+4mRObREKykl6KJSyoXQmKiumrcD+mbfI80xR7ufhftQKwXthVt5kUJ4rnaBqsWFXJbRSA9dJuzr5a/cS4j3rhNs2It5MNVSShVy/Nat8cAHl7UQDWC6wEBfEVZwkYpsFKzIS2QFBqTQBWhdXcM9O0uyCml0KFqzGTWY8DLOFafVQYopgURkd5ujd6gTczaw9to1Asps1IzFIgFq2qD7pIAovGbusmE1tj7DGQNVGhCRMx6O4CsAQkhf4RP31vJBDffPPNKa/uuGMmnD4ejanUR8+1kgRSTY7p/qqcnnPTjLpA2B3HHfts1hNpvHKNS98D/a0qTuaQLBXWVEYs4uRAcdn2Hps2NrF11T7JqEmqeD/0vTaLmsSsLBJzNtfe6ktep7GqHXN6V/K8MitlVLaCMifMKiTR3yhNQsSxVrLV3xKFpMszlROA1UYl976SMnn2l7qakwACcHPed0YNgaYm0BEBWOD0EqM4ntkXDPNu7AJsFmxrGuBYTtoCaX6ydenFLWpIYznFe5SgqheWWFaCg7RJZnYXi/m2WgHYTK4TW2Oc1quYxq2lKADHfrS2NfNmr8rMtL1+M31uocWW0KmXVL28mXOxVAMsoUhaNGlD1YbKmEffpLh/bQnA6kCl90xlFb9X8Y2z49JLs4RcvWjGZOsWkyOPPDLtn8pLqFEsaMX8LE7V9EHXWsgon1yI/dBz8sQTT3js2O4uAEc2ElSKNejxXKNvJfQprrWZRTfUUPX3SH9TWkuaEDNHgAXfp9bKt+ecLCZkqWPO6Hxiao899kj0d8iWMLSnulyvqYRRtQ1qvPrbXKkm2Mzik1NPPbXaZjpcvpJ7X0mZDneECpqaQA+N3n5cSRCAAAQgUCUBraF74403fM2ZPJxqHVt7k5wCaf2YCda+5rdcPfJYqjYVxmiBBRZwb8rlylabLw+dWveqNcddmcRCTsJWXnllX8NoL0Mel1khq6pxcFNqDNXcM8X41T3Rmkl5cC2XFIrKhHCPJSpnZVpz2Vqqpg+qxwTEoDZ0v0kQgAAEyhHQK70cvplg638zFN84+5ovr9GKGmCTae6lvpbrfsv1kXwI1AMBBOB6uAv0AQIQgAAEUgLFAnB6gh0IQAACEGiTgCbMFMtcDgdNG+8Oz+R9XbHVFQLMfCi0WQcFINDIBKoPmNbINBgbBCAAAQhAAAIQgAAEujEBWyrjHtXlVZ0EAQi0JEAYpJZMyIEABCAAAQhAAAIQgAAEIACBBiSABrgBbypDggAEINCdCWjNrYUUyXV9c3fmQd8hAAEIQAACEMiPAGuA82NJTRCAAAQgAAEIQAACEIAABCBQxwQwga7jm0PXIAABCEAAAhCAAAQgAAEIQCA/AgjA+bGkJghAAAIQgAAEIAABCEAAAhCoYwIIwHV8c+gaBCAAAQhAAAIQgAAEIAABCORHAAE4P5bUBAEIQAACEIAABCAAAQhAAAJ1TAABuI5vDl2DAAQgAAEIQAACEIAABCAAgfwIIADnx5KaIAABCEAAAhCAAAQgAAEIQKCOCSAA1/HNoWsQgAAEIAABCEAAAhCAAAQgkB8BBOD8WFITBCAAAQhAAAIQgAAEIAABCNQxAQTgOr45dA0CEIAABCAAAQhAAAIQgAAE8iOAAJwfS2qCAAQgAAEIQAACEIAABCAAgTomgABcxzeHrkEAAhCAAAQgAAEIQAACEIBAfgQQgPNjSU0QgAAEIAABCEAAAhCAAAQgUMcEEIDr+ObQNQhAAAIQgAAEIAABCEAAAhDIjwACcH4sqQkCEIAABCAAAQhAAAIQgAAE6pgAAnAd3xy6BgEIQAACEIAABCAAAQhAAAL5EUAAzo8lNUEAAhCAAAQgAAEIQAACEIBAHRNAAK7jm0PXIAABCEAAAhCAAAQgAAEIQCA/AgjA+bGkJghAAAIQgAAEIAABCEAAAhCoYwIIwHV8c+gaBCAAAQhAAAIQgAAEIAABCORHAAE4P5bUBAEIQAACEIAABCAAAQhAAAJ1TAABuI5vDl2DAAQgAAEIQAACEIAABCAAgfwIIADnx5KaIAABCEAAAhCAAAQgAAEIQKCOCSAA1/HNoWsQgAAEIAABCEAAAhCAAAQgkB8BBOD8WFITBCAAAQhAAAIQgAAEIAABCNQxAQTgOr45dA0CEIAABCAAAQhAAAIQgAAE8iOAAJwfS2qCAAQgAAEIQAACEIAABCAAgTomgABcxzeHrkEAAhCAAAQgAAEIQAACEIBAfgR651cVNUEAAhCAAAQgAAEIVEJg9Og3wuKLLx169OgVegT72LZnj56hl2/7hJ59eofDDjswnHf2SDvbZ+r5pG/olfQOPfWZ0if0mmLlbWv/h972mfp/T9tXjj497H8dq4Vgez18X9oPHa+762rh5ftfCWM/+jL0tsyeltm7h53rmUzd9khCHzvu3XOKl+9j254632OKnVeZqZ9elq/reivfy2pr52x/yQN3CKP/dFXo3UtlJ9tW9U+yj+qZHHr11rVWfy/72L7q7tXL8q1cT5VTeTvuoXK9J1sj2iZ2nFh/J4eesw0IfZcaESY/c5XlBftY2T62tetCH5XrEXr01b53PIRprGHrRNLXCvSZ+kn69gxJH7uoVx/L7xOmGPvQZ5owpa9daMd9ZtkhTPz+7jClt5Xp3c/qn86204Y+tt+r53TGdBrj81M1Yh8SBCBQ7wQQgOv9DtE/CEAAAhCAAAQajkCSJGHSpEkm2JqQFuxjWwnAiW9NYJWkaUllVMLPJ3Y+McFQnyl21RQT5EwIlkCrNLUmO2l7U/9JANbx1Lq8Hm9NpVUqhClTpoTJk0xotEpUdw8VtQZ8632xPBNklXraNrHzPUxInWLn4kfnrTv2sa3t9wja2nm/zo6s/imJymir+iZZXdaeHVsp29rHzv//vvLVH41d/TTBV2Ws/NStKtEIrdwUlbV/kydZnhVWGQHRvhrzjmnfO25bO2n5iZ/Tee0bJZ035okNUBxCYmyNd9BHh+pfYnVnPlPz1K+pfLwg/0EAAnVPYOq3uu67SQchAAEIQAACEIAABCAAAQhAAAIdI4AA3DF+XA0BCEAAAhCAAAQgAAEIQAAC3YQAAnA3uVF0EwIQgAAEIACB5iEg0+SXXnylpgP+aPTHYcL4CTVt46tX3qhp/WHid2HKJ2/WtI3JE9+2+jFzrilkKodAJxJgDXAnwqYpCEAAAhCAAAQgUAkBrRG+4/Z7bSmrvDnVJr3y8OteuxxT1Sp9ePej5tiqVrXbktzvvg6T33jUnIbVro1JE54xZ1jT1K4BaoYABDqVABrgTsVNYxCAAAQgAAEIQAACEIAABCDQVQQQgLuKPO1CAAIQgAAEIAABCEAAAhCAQKcSQADuVNw0BgEIQAACEIAABCAAAQhAAAJdRQABuKvI0y4EIAABCEAAAhCAAAQgAAEIdCoBBOBOxU1jEIAABCAAAQhAAAIQgAAEINBVBBCAu4o87UIAAhCAAAQgAAEIQAACEIBApxJAAO5U3DQGAQhAAAIQgAAEIAABCEAAAl1FAAG4q8jTLgQgAAEIQAACEIAABCAAAQh0KgEE4E7FTWMQgAAEIAABCEAAAhCAAAQg0FUEEIC7ijztQgACEIAABCAAAQhAAAIQgECnEkAA7lTcNAYBCEAAAhCAAAQgAAEIQAACXUWgd1c1TLsQgAAEINBcBMaOHRuefPJJH/Taa68devfmJ6g9T8Djjz8exo0bV9GlK664YphpppkqKtuohX744YfwwAMPOAfxIEEAAhCAQHMT4O2jue8/o4cABCDQaQQuuuiicMwxx3h7N9xwQ9hmm206re1Gamj//fcPzz//fEVDeuSRR8Kqq65aUdlGLfTVV1+F9ddfPwwbNiydgOkOY+3Ro0dYc81Vw0MPPFGz7i40bIHwyeiPw8Rx39asjTmHLx8+f+SpmtUf+vUPvQYtHJIPatdGr2mWCpOTN2o3BmqGAAQ6lQACcKfipjEIQAACzUvgsssuCz/60Y/C+++/H/70pz8hAHfwUdh9993D3HPP3WotgwYNavV8M5zs1atXmHfeecOcc87ZrYbbs2fPsOJKQ2sqAA9ecp4w7pOxNRWAZxu6RE0F4B7TTB96Dlo8TK6hANx7miFh8oQx3er5obMQgEB5AgjA5dlwBgIQgAAEciIgTeRrr70WjjzyyPDCCy+Eu+++O7z++uthyJAhObXQfNUceOCBrtVsvpFXN+JZZ501vPvuu9VdRGkIQAACEGhYAjjBathby8AgAAEI1A8BaX+VNtxww/DjH/84JEkSZBJdnCZPnhxksjp+/PjiU+mxzn/99dfpcdzRNY8++mh46qmnwnfffRezC7bK1/Vq/5tvvgkSzCdMmFBQRgf//e9/g9baau3oBx984OVbFMpkaE3ugw8+GD799FPPnTRpkrfz/fffZ0pN3VXbb731Vrj33nvDe++91+J8LTI0Zn20HrY4afw6Jx5K6p+OI0OdF6dyfa2UaTXj/uyzz8LDDz/sny+//LK4ywXHbZWN4yn3TOme6d49/fTT6ZgLGrCD7Bh1Ts/H/fffH9588802n43iujiGAAQgAIGuJYAA3LX8aR0CEIBAwxOQ4HH99dcHaeJWX331sPXWW4dpppkm/PnPf24hfEoIk9muTKUnTpzYgs0TTzwRZplllrDTTjul5yQgSaieccYZw2qrrRbk6GiGGWYIhx9+eCgWQA866CC//p577gkDBw4Mw4cPD7PPPnu6NlROupZaainv6yqrrBLkrGueeeYJCy20kAvDaaP/25E2W2Vmm222sNZaa7mZ7WabbRb++te/ejsXXnhhwSV/+9vfwlxzzRUWXHDBsO6664bBgwe7FlzCdi2TtMXF3NSeBPfFF1/cz0kgV5JAqLJidc4557jzKHFSX5deemm/l17wf/+1xVTFKh23hO2dd97ZTbv1rOgjtiuttFJ49dVXs836s1NJ2c8//9zHM2LEiILrZYGw8cYb+z3TvdMaYT03Gk+cDIgX6FkSk9GjR/skjvqk+vRcyIrh2WefjUXZQgACEIBAvROwmVESBCAAAQhAoGYELr300sR+C5MDDjggbWO77bbzPBOC07y4s+uuu/q5v//97zEr3Zog5+fMiZbnmXCdzDfffJ5nwlBiglZy3XXXJZtuuqnnrbfeeum12tlrr70834S5xNaEJptsskliwmhignJiwm9i60UTE3QSE3iSG2+8MTEBMDEB169RvgmMaX0mWHnbusZMu5Nbb701OfvssxMTjpLpp5/erznvvPPS8hdccIHnmeCdnH/++cmdd96ZnHHGGYl5afZ2Tduclm1tZ9lll/V61N9Kk2l0E41Z9yGy07WR9cEHH5xW9fHHH3s5jcPWoSbHHXdcYpr1RPdKzGzyIrGJiLR8a0xVqJpx/+QnP/G2t9xyy0TPjU0kpPdswIABiU2KpO1WWtYEeq/TBNz0Wt07m4jwfFtL7fd61KhRyZprrpmWNS1+Wn6//fbzfBN4/f6akJyYBUOi50tMbS12Qd/SC1vZmTx5SvLVV2MT845e9Pnajos/4yxvXPK1f8bbNvP56pvka/uMK/EZ/9W3SfHnG8sr/nw79tuk+POd5RV/Jnz9TZL9fG/HxZ+Jlpf9/PD1+OSHcYWfSXb8/59xyaTx45LJpT7fjEum+Odr2/7v861tM5/ku6+TqZ+xtv3fZ4Jt4+d7289+JtrxRLtGnx8yn0n23c5+Jo9PrFP2+Saxxv0zZcq3yZTkW7ur39lnwv8+39tWHxIEINBdCMh0hwQBCEAAAhCoGQHTyrqQkBXYbr/9ds8zzV6Ldu+77z4/t+222xack/Ajocw0ti6w6qSEMwkgu+22W0HZKVOmJBKidE5CVExRWJPQbGbUMdu3O+64o5c3B10F+TpYZJFF/Jz6HZP6p/olqGWTaRZdSNS5c889109JCJt55pldoHzjjTeyxZPnnnvO61lmmWWSrNBVUChzEAVg09wmEurKfcQmmyRgS6CVEGta8+Taa6/1dlWfaV7TolEAzvY/nnz55ZddWDenUomZq3t2a0yrHbcmGSRMmql2bNK3mtDQfTez6DS/0rKlBOAddtjBx/6rX/0qrU874h8nT37/+9+n56IAvMACCyTffisBaGpSP5dffnmv66677orZbCEAAQhAoI4JIADX8c2haxCAAAS6OwEzW3XhQMJaNknQkKAjIUsCYDZJeJ1//vmTfv36mYbsq/TUzTff7OUPOeSQNE9Ckeqwdb9pXtyJ5c0kOWal2sQTTzwxzYs7EkyllS0lhB566KHejoTGmCTQmql2KgjGfG332WcfLx8F4D/84Q9+LI1zqbTccsv5+WeeeabU6YK8KABr3K19JOQVJwtD5deYGbpruvv375+Yc7KCYlEAlqAszXhx0rVqV/dWKQrApZhWO25pWKVRv/LKKwuEYD0TxanSssUCsAR3PVuaTLG1vcXVJi+++KKPb4UVVkjPRQH4N7/5TZoXd6QNFo+rrroqZrGFAAQgAIE6JoAXaPvVIkEAAhCAQG0IXH755V6xCXju+TnbigkYwcyGPSRS1iGW4p/uscce4aSTTgpmhhxMwPLLzETVt3vuuadv5ahJ6zunnXbacMkll/gnW39c/6t1m8Vp4YUXLs7ydblamyunSv/+97+DaTvdU7XWd8qxllJ0IiUnVmpfMXYVrqY4mTa3ICv2Qc67TJgqOKeDWK/KDR06tMX5Uhlar6x1q9WkX/7yl8E0leGmm27yy6644oqynri13rdv374tql9iiSX8esUiNs14er4U02rHfcQRRwQzlQ9mmu1rcbVOeqONNgpaVz3HHHOkbWmnmrLZC8eMGePrh7VW3ATh7Cnf1/iUL6/lxckmZoqzfE23MqPTsBYFyIAABCAAgboigABcV7eDzkAAAhBoHAKmSQ0SsJSuvvpq/5Qa3TXXXBPOPPNMd2IVzyvG7cknnxxMq+YCsITN2267LZj20z8qF0PbyFmW6iiV5NSolBfhUvFzJZzuu+++7sBK3qiVTEPqTrUkaD722GNpE/L+q2Trd9O87I5pF7OHaV9NwxskOJZK6qtpX0udyi1PAq0EStO6B8XH1cREuSRnXaVSHFvkH8uUYhrLVDpuTQ7I6ZitpQ7/+te/fAJEkyDqq5xTnXXWWaF376mvLtWUjX3UNnqzNg13Njvd1wSMrTf2cnKGZeu503Ol7rfKK5myIy3HDgQgAAEI1C8BBOD6vTf0DAIQgEC3JnDHHXe4QGdrJF14KTUYcxLl2lYzeQ3yVByTvECvs846wdYDexiif/zjH+7ROWp/VU5enJUWW2yxYGarvl/pf1FoyZaXJ2kzgQ7mCCmYg6UgL9DSCKvs/vvvXyAAyyOykkIklUrF+bGv0mJvs802pS7plDwJoppskIAnTfcuu+ziHrDllbs46Xyp9OGHH3r2kksuWXC6FNP2jNvW4AZ9NOkhz9S27to9T//ud7/zfv/iF79I262mbLxIArbSRx99FLMKtmZu7c+tmGSF34JCHEAAAhCAQLcl0NJuq9sOhY5DAAIQgEA9EYixf2XSKpPmUh/zPuxdLg4XpEwJuxJGFELnL3/5i5vjZsMfKXyROULy8DhffPFFi6FL+3jKKacE8ybd4lxxhq0TdeFXgqFCJMkEVyFuolD3yiuv+CXSaiuZMyQPmSNtrgS14qQYsdmkUDlKiqdbKslUXALe+++/X+p0LnnmvMlDDGkMtpY52DrloDBOWYEy29BLL70U4niz+dEcXBMbbaVqxi3ttxhEqwFbY+2TBeZkLERTeplvK1VTtriP8803X+jTp0/4z3/+0yLckcrK5F0m6YsuumjxpRxDAAIQgEADEEAAboCbyBAgAAEI1BuBTz75xE2WtT7XvCWX7Z60rtKySdgyD78F5RQvWCanEoAefPBB1wpK6M2mvffe24U0rQctNkHVOZlRlzKBztahfQtF41kSfBSLNpskgEfBNa7VlQB12GGHBZlNWwikEE2mdZ1iAEtjnU3bb7+9C8zmYdrXFmfPydRXGmcLF+QxibPn8txXP7WuVWbe0q7LnFjaUMX6laa9OEkYHzlyZEG2Jgck3Gv9bPGa3IKC/zuoZtxaS637KNPmqGWOdcZ7EtcZV1M21hG3MqHWZMx///vfcOqpp8Zs3+r+Hnvssb4v7TgJAhCAAAQaj0DvxhsSI4IABCAAga4mIJNmaQ9l7jvjjDOW7Y7W2EpIkoZPwuHw4cPTsnJEZKGJQtQOZ82fY6ETTjghWFxb1xpK42uej91UWk6eLOxPGDFiROpEK15Taittr3mqduFUgreEH5nAqg5psiUovv3220Ga4pgkrP3zn/90AV3rg+UQS86xJCBKy6jy0UGW1tP++te/DtJ4W+inYF6lg5wtSbCWdltJwmYpp0yxveKthX4K0003XXF2wbHYmudnNyMWX5mWywRaSfdFbWpNsNZcSxssjXpMcd2tNKUWysrPm1drNz2PTrRi2XLbasatsWvSQg7R9BxstdVWvt5bEyO6x5pMiQ7RJHxXWqsAaQkAAAoeSURBVLZU33QvbrnllmBenX1SYPPNNw/SkFusY3d4pnZ0f0kQgAAEINCABOrYQzVdgwAEIACBbkrA1uV6aBhzXNXmCEwD6mVN4EwUsiabnnjiCT9nglRBWJxsGTNBTkw4TmPv2k91YqbLieL0msOjbNE0ZI+tLS3I14HC+ti6X29PdehjgmxiAmqiEEk6XnnllQuuU/zco48+OjHPzYk5sfLzCoej+LIqb4J9QXnTDCe2rrigjUGDBrWIJVxwUdFBpWGQ1L6ZnztThTQSE9PgFtWWJKYN9f6YNt7PxTBIitFs2uHENPR+3gTUxITlFmGrYhikUkxjY5WOW7GeTVOdtqkx2CRCohjJTz/9dKzOt5WWLQ6DFCtR/nbbbZeYNj+9H2b2nJhJeIvQVqaV9jIKk1WcTjvtND938cUXF5/iGAIQgAAE6pBAD/XJfmBIEIAABCAAgW5NQGbIJqh6OBqt0W1N89zaQGV+KydW0uJqTXC5JHNZmUKXSjI3lidjrT/eYostWhSR+a36Kk2mtMvSttZLkvm6NLcm7LvjL2nyFRJKWvK2NM5tjaHScSuElTT6Mk2Xk7PWnFFVU7ZU/3QfbfLDzc9LebIudU1n5inUl54VaefllC16wc67DzLn11IEWTLklbQ+/p133glymFbLZ1xr8fXM6pN36iz+efeb+iAAgfIEEIDLs+EMBCAAAQhAoCyBpZZaytcLy0Q3G1JHL/0yb5YgLU/DtXgpL9upHE4UC8A5VEkV7SAwbtw4Xw8th2VRVzHvvPMG0zSHDTfcsB01lr9Ek0ebbLJJUHivGLu5fOm2z2j9uJysyYu3Jhi01EFr+WWSn3fSMoX11lvPzdd33nnn3KrvTP65dZqKIACBigjgBKsiTBSCAAQgAAEIFBJYa621XDOntbjS9I4ZM8bX88rpl4TfQw45pNsJv4Uj5KgrCWi9uNY+y0mZvF7LUZkEYK2rj3Go8+ifNL9yCqb17HklOTKTFYXCk8kJndZUaz1/NpZ2Hm3dfffd7mSvlLfyjtbfWfw72k+uhwAE2kGgDs2y6RIEIAABCECg7gmY06TE4tCm60ftJ9j3zdQzsZjGiYVwqvsxlOpgXANcvN65VFnyakNAz1bfvn0TcwRW0MDjjz/uz5g5MivIb++B1oSbCX5i3taTRRZZJDEz9/ZWlV536623eh8tfnOap++COWBLzEldmteRne+++87X/es7Zx7JvT2tvc8rdRb/vPpLPRCAQHUE8ALdjkkDLoEABCAAAQjIK7G97LuWS7FjpelabrnlwrBhw1pds1rv5Gadddbw0EMPtXsNdb2Przv0T/Gv5UldnsmzKYaeksfqPJK8Xiue83nnnReOP/74YE7nOlyttLIyeV5//fXTuhRPW562R40aleZ1ZEcm1uaQzD3Hr7vuuiGGx+pIndlrO4t/tk32IQCBziOAANx5rGkJAhCAAAQakIDWAuvTKEmOvVZfffVGGU63HIecfpVaL6t1tEoyv88jnX/++WHmmWfOo6q0Djns0pr4YqdXAwcOdKdmpr31kFbpBe3YkSm4Qo4pdJbayzt1Fv+8+019EIBAZQRYA1wZJ0pBAAIQgAAEIACBLiPw4IMPhrPOOsvX0q6xxhq59CNv4Ved0pri2WabrUX/ZFmgpPXMHU2K0V1NzOyOtqfra8E/j35RBwQgUD0BNMDVM+MKCEAAAhCAAAQgkAsBhZiSyW1Mth7XnV3FY23vuOMO1whbDOhw4YUXZk+1ua8wPsVC55AhQ4KtMW7z2vYUkHBqMZpbXGor9DyvFg6rWjSWc0ZH+OfcFaqDAARyIIAGOAeIVAEBCEAAAhCAAATaQ0BrxqMZvbbHHHNMQTWXXXaZr59dbbXVgjmtqnpt9uWXX15Qv9p4++23C9rI80CmzloPX5xingT87pQ6yr87jZW+QqBZCKABbpY7zTghAAEIQAACEKg7Aua9OGS1ooMHD077KOdUhx12WNh7773d4VPv3tW/tsn51HzzzZfWqR0JqbVKqluxpDWmbH/luGqWWWYJAwYMqFXTudebB//cO0WFEIBAhwlU/5e0w01SAQQgAAEIQAACEICACFioo5IgRo4c6cLviSeeGE455ZSSZSrJtPBGQZ/OShYaLJxxxhlB3qA32mgjb1Ym3oqVvcEGGwR5hO4OKS/+3WGs9BECzUYAAbjZ7jjjhQAEIAABCECgrglIg3rUUUe5plaOqs4999yC/i6zzDJhxIgRBXn1cjB8+PCw0korBYuFHa6++upgcYbDSSedFMaPH+9OvOqln631ozvzb21cnIMABKYSQADmSYAABCAAAQhAAAJ1RODmm2/2kEFjx44Nhx9+eIue7bfffnUrAKuzN910k3urXnXVVV3ju+KKK3pc40GDBrUYSz1mdHf+9ciUPkGgngj0MK98U93y1VOv6AsEIAABCEAAAhCAQLcmIMdXEyZMqOma424NiM5DAAJdQgABuEuw0ygEIAABCEAAAhCAAAQgAAEIdDYBwiB1NnHagwAEIAABCEAAAhCAAAQgAIEuIYAA3CXYaRQCEIAABCAAAQhAAAIQgAAEOpsAAnBnE6c9CEAAAhCAAAQgAAEIQAACEOgSAgjAXYKdRiEAAQhAAAIQgAAEIAABCECgswkgAHc2cdqDAAQgAAEIQAACEIAABCAAgS4hgADcJdhpFAIQgAAEIAABCEAAAhCAAAQ6mwACcGcTpz0IQAACEIAABCAAAQhAAAIQ6BICCMBdgp1GIQABCEAAAhCAAAQgAAEIQKCzCSAAdzZx2oMABCAAAQhAAAIQgAAEIACBLiGAANwl2GkUAhCAAAQgAAEIQAACEIAABDqbAAJwZxOnPQhAAAIQgAAEIAABCEAAAhDoEgIIwF2CnUYhAAEIQAACEIAABCAAAQhAoLMJIAB3NnHagwAEIAABCEAAAhCAAAQgAIEuIYAA3CXYaRQCEIAABCAAAQhAAAIQgAAEOpsAAnBnE6c9CEAAAhCAAAQgAAEIQAACEOgSAgjAXYKdRiEAAQhAAAIQgAAEIAABCECgswkgAHc2cdqDAAQgAAEIQAACEIAABCAAgS4hgADcJdhpFAIQgAAEIAABCEAAAhCAAAQ6mwACcGcTpz0IQAACEIAABCAAAQhAAAIQ6BICCMBdgp1GIQABCEAAAhCAAAQgAAEIQKCzCSAAdzZx2oMABCAAAQhAAAIQgAAEIACBLiGAANwl2GkUAhCAAAQgAAEIQAACEIAABDqbAAJwZxOnPQhAAAIQgAAEIAABCEAAAhDoEgIIwF2CnUYhAAEIQAACEIAABCAAAQhAoLMJIAB3NnHagwAEIAABCEAAAhCAAAQgAIEuIYAA3CXYaRQCEIAABCAAAQhAAAIQgAAEOpsAAnBnE6c9CEAAAhCAAAQgAAEIQAACEOgSAgjAXYKdRiEAAQhAAAIQgAAEIAABCECgswn8H0xuU9KDu6pzAAAAAElFTkSuQmCC" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
print(dot_plot_male)
## plot female
dot_plot_female <- DotPlot(mutant_female_seurat, assay = "RNA", features = mca_female_core_genes, group.by = "identity_name_combined") +
theme_classic() +
# change appearance and remove axis elements, and make room for arrows
theme(axis.text.x = element_text(size=16, angle = 45, hjust=1,vjust=1, family = "Arial"), text=element_text(size=16, family="Arial"), legend.position = "bottom", legend.direction = "horizontal", legend.box = "vertical", plot.title = element_blank(), plot.margin = unit(c(1,3,1,3), "lines")) +
#change the colours
scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white", begin = 0, end = 1, direction = 1) +
## change x axis label
labs(x = "MCA Core Genes", y = "Genotype", title = "Expression of Female MCA Core Genes in Feale Mutant Cells") +
## change label on bottom of plot so we can indicate markers
#scale_y_discrete(labels = c("fd1", "fd2", "fd3", "fd4","md1", "md2", "md3", "md4", "md5")) +
coord_flip()
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
## view
print(dot_plot_female)
save
ggsave("../images_to_export/GCSKO_sexbranch_dot_male.png", plot = dot_plot_male, device = "png", path = NULL, scale = 1, width = 30, height = 60, units = "cm", dpi = 300, limitsize = TRUE)
ggsave("../images_to_export/GCSKO_sexbranch_dot_female.png", plot = dot_plot_female, device = "png", path = NULL, scale = 1, width = 30, height = 60, units = "cm", dpi = 300, limitsize = TRUE)Pheatmap version of heatmap old cold to make main heatmap (NOT RUN)
Make annotations to add to the heatmap
## change names for row names to include "module " at the begining of them
row.names(agg_mat) <- stringr::str_c("Module ", row.names(agg_mat))
## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module$Freq)){
row.names(agg_mat)[i] <- stringr::str_c(row.names(agg_mat)[i]," (n = " ,genes_per_module$Freq[i], ")")
}
## create annotation of clusters for pheatmap:
cluster_anno <- data.frame(cluster = unique(colData(monocle.object)$seurat_clusters))
row.names(cluster_anno) <- cluster_anno$cluster
## clusters were defined earlier as:
male_clusters <- c("13", "5", "11", "25", "1", "17", "10", "24", "14", "27", "19")
female_clusters <- c("16", "22", "23", "15", "21", "30", "4", "3", "18", "7", "8", "6", "20", "12", "26")
asex_clusters <- c("0", "9", "28", "2", "29")
## add identities to the column
cluster_anno$group <- NA
cluster_anno$group[which(cluster_anno$cluster %in% asex_clusters)] <- "Asexual"
cluster_anno$group[which(cluster_anno$cluster %in% male_clusters)] <- "Male"
cluster_anno$group[which(cluster_anno$cluster %in% female_clusters)] <- "Female"
cluster_anno <- cluster_anno[ , 2, drop = FALSE]
cluster_anno
## add median pseudotime per cluster
## help here:
## https://stackoverflow.com/questions/54360855/calculate-mean-for-column-grouped-by-values-of-two-other-columns
## make subsetted dataframe
df_median_pt <- meta_data_df[ ,c("pt", "seurat_clusters")]
## apply across dataframe to get median
mean.df1 <- tapply(df_median_pt$pt, list(df_median_pt$seurat_clusters), median)
mean.df2 <- as.data.frame(as.table(mean.df1))
names(mean.df2) <- c("seurat_clusters", "pt_Median")
rownames(mean.df2) <- mean.df2$seurat_clusters
## to make each value have the mean in the OG dataframe
#merge(df_median_pt, mean.df2)
## add to annotation dataframe
cluster_anno <- merge(cluster_anno, mean.df2, by=0)
## add rownames to dataframe
rownames(cluster_anno) <- cluster_anno$Row.names
## subset to have only info of interest
cluster_anno <- cluster_anno[,c(2,4)]
names(cluster_anno) <- c("Identity", "Median_Pseudotime_of_Cluster")WT cells by modules over pt (central panel)
## make annotation colours
annotation_colours <- list(Identity = c(Male="#016c00", Female="#a52b1e", Asexual= "#0052c5"),
Median_Pseudotime_of_Cluster = magma(12, direction = 1))
## reorder the levels
## make df of data
agg_mat_df <- as.data.frame(agg_mat)
## remove levels in my_levels that are not present here - i.e. clusters that are missing because they are not represented in the 10X data
my_levels_10x_data <- my_levels_sex[which(my_levels_sex %in% colnames(agg_mat_df))]
## sort the values
agg_mat_df <- agg_mat_df[ ,(match(my_levels_10x_data, colnames(agg_mat_df)))]
## order
#cluster_anno <- cluster_anno[(match(my_levels_10x_data, rownames(cluster_anno))), ]
## reorder columns
## first, order the annotation
cluster_anno <- cluster_anno[with(cluster_anno, order(Identity, Median_Pseudotime_of_Cluster)),]
## remove the NAs from this
cluster_anno <- cluster_anno[complete.cases(cluster_anno),]
agg_mat_df <- agg_mat_df[ ,match(rownames(cluster_anno), colnames(agg_mat_df))]
## plot heatmap
pheatmap::pheatmap(agg_mat_df,
scale="row",
cluster_cols = FALSE,
clustering_method="ward.D2",
annotation_col = cluster_anno,
annotation_colors = annotation_colours,
cutree_rows = 12)
#, gaps_col = c(28,29,37)#row.names(agg_mat) <- factor(row.names(agg_mat), levels = row.names(agg_mat)[module_dendro$order])
## plot heatmap
#pheatmap::pheatmap(agg_mat_df,
# scale="column",
# cluster_cols = TRUE,
# cluster_rows = module_dendro,
# #clustering_method="ward.D2",
# cutree_rows = 5,
# annotation_col = cluster_anno,
# annotation_colors = annotation_colours) +
# theme(legend.position = "bottom")---
subtitle: 'Gametocyte Development in <i>Plasmodium berghei</i>'
title: |
  ![](../GCSKO_logo.jpg){width=300px}  
  Pseudotime Sexual Branch
author: "[Andrew Russell](https://ajcrussell.wixsite.com/mysite/about)"
institute: Wellcome Sanger Institute
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output:
  html_notebook:
    theme: cosmo
    toc: yes
    toc_depth: 3
    #toc_float: yes
    df_print: paged
---
***
# 1. Introduction and Aims {.tabset}

We have merged the two datasets together in GCSKO_merge.Rmd. We also subsetted out the pre-sexual-branch and the sexual cells (male and female) and stored them in a Seurat object called tenx.mutant.integrated.sex. Here, we will perform pseudotime analysis on the dataset and build modules of genes that show similar expression across this pseudotime.

# 2. Read in the data  {.tabset}

## Load/Install the Required Packages

```{r load packages, echo = FALSE}
## CRAN packages

## Patchwork is needed to stich plots together using '+'
if(require("patchwork", quietly = TRUE)){
    print("patchwork is loaded correctly")
} else {
    print("trying to install patchwork")
    install.packages("patchwork")
    if(require(patchwork)){
        print("patchwork installed and loaded")
    } else {
        stop("could not install patchwork")
    }
}

## viridis allows different colours to be added to plots
if(require("viridis", quietly = TRUE)){
    print("viridis is loaded correctly")
} else {
    print("trying to install viridis")
    install.packages("viridis")
    if(require(viridis)){
        print("viridis installed and loaded")
    } else {
        stop("could not install viridis")
    }
}

## Seurat is needed for most of this script
if(require("Seurat", quietly = TRUE)){
    print("Seurat is loaded correctly")
} else {
    print("trying to install Seurat")
    install.packages("Seurat")
    if(require(Seurat)){
        print("Seurat installed and loaded")
    } else {
        stop("could not install Seurat")
    }
}

## cowplot is needed for plots in this script
if(require("cowplot")){
    print("cowplot is loaded correctly")
} else {
    print("trying to install cowplot")
    install.packages("cowplot")
    if(require(cowplot)){
        print("cowplot installed and loaded")
    } else {
        stop("could not install cowplot")
    }
}

## gridExtra is needed for grid graphics to plot multiple plots in the same view
if(require("gridExtra")){
    print("gridExtra is loaded correctly")
} else {
    print("trying to install gridExtra")
    install.packages("gridExtra")
    if(require(gridExtra)){
        print("gridExtra installed and loaded")
    } else {
        stop("could not install gridExtra")
    }
}

## grid is needed for grid.arrange function to change size of title
if(require("grid")){
    print("grid is loaded correctly")
} else {
    print("trying to install grid")
    install.packages("grid")
    if(require(grid)){
        print("grid installed and loaded")
    } else {
        stop("could not install grid")
    }
}

##for doing bulk correlation calculations
if(require("Hmisc")){
    print("Hmisc is loaded correctly")
} else {
    print("trying to install Hmisc")
    install.packages("Hmisc")
    if(require(Hmisc)){
        print("Hmisc installed and loaded")
    } else {
        stop("could not install Hmisc")
    }
}

## reshape2 to melt dataframes for plotting:
if(require("reshape2")){
    print("reshape2 is loaded correctly")
} else {
    print("trying to install reshape2")
    install.packages("reshape2")
    if(require(reshape2)){
        print("reshape2 installed and loaded")
    } else {
        stop("could not install reshape2")
    }
}

## to work with data frames:
if(require("dplyr")){
    print("dplyr is loaded correctly")
} else {
    print("trying to install dplyr")
    install.packages("dplyr")
    if(require(dplyr)){
        print("dplyr installed and loaded")
    } else {
        stop("could not install dplyr")
    }
}

## this allows us to use the colorRamp2 function which allows us to plot nice colour gradient pals:
if(require("circlize")){
    print("circlize is loaded correctly")
} else {
    print("trying to install circlize")
    install.packages("circlize")
    if(require(circlize)){
        print("circlize installed and loaded")
    } else {
        stop("could not install circlize")
    }
}

## non-CRAN packages

## monocle3 to calculate pseudotime:
if(require("monocle3")){
    print("monocle3 is loaded correctly")
} else {
    print("Please install monocle3 (https://cole-trapnell-lab.github.io/monocle3/docs/installation/)")
}

## destiny is used for diffusion plots (DiffusionMap function)
if(require("destiny")){
    print("destiny is loaded correctly")
} else {
  print("trying to install destiny")
  if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
  BiocManager::install("destiny")
  if(require(destiny)){
        print("destiny installed and loaded")
    } else {
        stop("could not install destiny")
    }
}

#set the seed for both the mixture models and also for the sample function later on:
set.seed(-92497)
```

## load data

```{r}
## load sex only branch cells saved from GCSKO_Sex_Branch_Analysis.Rmd
## Restore the objects
## load sex branch dataset
tenx.mutant.integrated.sex <- readRDS("../data_to_export/tenx.mutant.integrated.sex.RDS")
## load full dataset
#tenx.mutant.integrated <- readRDS("../data_to_export/tenx.mutant.integrated.RDS")
```

```{r}
## add old pt values
## these values are calculated in hte GCSKO_pseudotime_allcells.Rmd script and so were added after the object was split into sex only.
tenx.all <- readRDS("../data_to_export/tenx.mutant.integrated.RDS")
tenx.all.meta <- as.data.frame(tenx.all@meta.data)
tenx.all.meta <- tenx.all.meta[which(rownames(tenx.all.meta) %in% rownames(tenx.mutant.integrated.sex@meta.data)),]
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, tenx.all.meta$old_pt_values, col.name = "old_pt_values")
## then remove these objects so they don't use up memory
rm(tenx.all, tenx.all.meta)
```

Add a meta.data column so that 10X is listed as WT:
```{r}
## get cells that are filtered out
mutant_cells <- which(tenx.mutant.integrated.sex$experiment == "mutants")

## make extra column in plotting df
tenx.mutant.integrated.sex@meta.data$identity_name_combined <- "WT_10X"
tenx.mutant.integrated.sex@meta.data$identity_name_combined[mutant_cells] <- tenx.mutant.integrated.sex@meta.data$identity_name_updated[mutant_cells]

## check
table(tenx.mutant.integrated.sex@meta.data$identity_name_combined)
```

```{r}
## create a list of our mutant gene IDs
list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")
```

Read in gene annotations
```{r}
gene_annotations <- read.table("../data/Reference/GenesByTaxon_Summary.csv", header = TRUE, sep = ",", stringsAsFactors = TRUE)
dim(gene_annotations)

## convert _ to -
gene_annotations$Gene.ID <- gsub("_", "-", gene_annotations$Gene.ID)
```

# 3. Dimensionalty Reduction {.tabset}

We will now re-calculate the UMAP, PCA and diffusion map to visualise the data since the old visualisation used the variation in the whole dataset and so some of the variation in this set of cells was obscured.

ref: https://github.com/satijalab/seurat/issues/1883

## A. Recalculate PCA

The PCA is used as the basis of other dimensionality reductions so we will now recalculate this to get to our final UMAP.

First, run PCA again
```{r}
tenx.mutant.integrated.sex <- RunPCA(tenx.mutant.integrated.sex, npcs = 30, verbose = FALSE)
```

Then inspect the PCs
```{r}
ElbowPlot(tenx.mutant.integrated.sex, ndims = 30, reduction = "pca")
```

Have a quick look at the output
```{r}
FeaturePlot(tenx.mutant.integrated.sex, reduction = "pca", pt.size = 0.01, features = "old_pt_values")
```

Run tSNE
```{r}
tenx.mutant.integrated.sex <- RunTSNE(tenx.mutant.integrated.sex, seed.use = 1234, perplexity = 200)
FeaturePlot(tenx.mutant.integrated.sex, reduction = "tsne", pt.size = 0.01, features = "old_pt_values")
```


## B. UMAP

calculate UMAP
```{r, fig.height = 7, fig.width = 7}
## run UMAP
#tenx.mutant.integrated.sex <- RunUMAP(tenx.mutant.integrated.sex, reduction = "pca", dims = 1:15, n.neighbors = 20, seed.use = 1234, min.dist = 0.5, repulsion.strength = 0.05, reduction.name = "umapoptimised_post_repca")

tenx.mutant.integrated.sex <- RunUMAP(tenx.mutant.integrated.sex, reduction = "pca", dims = 1:11, seed.use = 1234, n.neighbors = 10, reduction.name = "umapoptimised_post_repca", reduction.key='umap_post_repca_')

## plot
DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "identity_name_combined") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, split.by = "genotype_combined") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, split.by = "experiment", group.by = "identity_name_combined") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

FeaturePlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", pt.size = 0.01, features = "PBANKA-1319500") + coord_fixed()
```


check markers to orientate
```{r, fig.height = 4, fig.width = 10}
plots <- FeaturePlot(tenx.mutant.integrated.sex, features = c("PBANKA-1319500", "PBANKA-0416100"), blend = TRUE, combine = FALSE, coord.fixed = TRUE, reduction = "umapoptimised_post_repca")

plots[[3]] + NoLegend()  # Get just the co-expression plot, built-in legend is meaningless for this plot
plots[[4]] # Get just the key
CombinePlots(plots[3:4], legend = 'none', ncol =2, nrow = 1, rel_widths = c(2, 1), rel_heights = c(4,1)) # Stitch the co-expression and key plots together
```

inspect mutants in 13 that are in between sexes
```{r}
## get mutant cells
mutant_sex_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$genotype_combined == "Mutant"),])
                                                        
## subset object
mutant_only_seurat <- subset(tenx.mutant.integrated.sex, cells = mutant_sex_cells)

## plot
umap_mutant <- DimPlot(mutant_only_seurat, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5) +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

## view
HoverLocator(plot = umap_mutant, information = FetchData(mutant_only_seurat, vars = c("ident", "identity_updated", "nFeature_RNA", "identity_name_combined")))
```

```{r}
DimPlot(mutant_only_seurat, label = TRUE, label.size = 8, repel = FALSE, pt.size = 0.5, dims = c(2,1), reduction = "DIM_UMAP") + 
     coord_fixed() +
     theme(legend.position="bottom", 
           axis.line=element_blank(),
           axis.text.x=element_blank(),
           axis.text.y=element_blank(),
           axis.ticks=element_blank(),
           axis.title.x=element_blank(),
           axis.title.y=element_blank()) + 
     guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))
```

# 4. Clustering {.tabset}

### Investigate sub clustering of pre-branch clusters

Ultimately, we are looking for coexpression after the expression of AP2G:
```{r}
## plot
marker_gene_plot_AP2G <- FeaturePlot(tenx.mutant.integrated.sex, features = "PBANKA-1437500", coord.fixed = TRUE, min.cutoff = "q1", dims = c(2,1), reduction = "DIM_UMAP", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("AP2G Expression")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

##view
marker_gene_plot_AP2G
```
and we have the following clusters currently:

```{r, fig.height = 4, fig.width = 4}
## Plot
umap_cluster <- DimPlot(tenx.mutant.integrated.sex, label = TRUE, label.size = 8, repel = FALSE, pt.size = 0.5, dims = c(2,1), reduction = "DIM_UMAP") + 
  coord_fixed() +
  theme(legend.position="bottom", 
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank()) + 
  guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))

## print
umap_cluster
```

11 is the common sex branch so we will interrogate that branch

```{r}
## subset cluster 13 cells
cells_bipot <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$seurat_clusters == 11 & tenx.mutant.integrated.sex@meta.data$genotype_combined == "WT", ])

## subset cluster 13
seurat_bipot <- subset(tenx.mutant.integrated.sex, cells = cells_bipot)
seurat_bipot

## re-PCA
seurat_bipot <- RunPCA(seurat_bipot, npcs = 30, verbose = FALSE)
ElbowPlot(seurat_bipot, ndims = 30, reduction = "pca")

## recluster
seurat_bipot <- FindNeighbors(seurat_bipot, dims = 1:15)
seurat_bipot <- FindClusters(seurat_bipot, resolution = 1, random.seed = 42, algorithm = 2)
DimPlot(seurat_bipot, reduction = "pca",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") 

## plot cluster resolution = 1
## plot
DimPlot(seurat_bipot, reduction = "DIM_UMAP",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```

2 and 1 are superimposed on the branch, lets find markers for each cluster

```{r}

# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

# PBANKA-1437500 - AP2G - commitment

## find markers
seurat_bipot_markers <- FindAllMarkers(seurat_bipot, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25, test.use = "MAST")

## inspect result
markers_subset <- seurat_bipot_markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_logFC) %>% filter(p_val_adj < 0.05)
markers_subset <- markers_subset[order(-markers_subset$avg_logFC), ]
markers_subset

## find markers
markers_subset_mutant <- markers_subset[which(markers_subset$gene %in% list_of_mutant_genes), ]
markers_subset_mutant

## plot
VlnPlot(seurat_bipot, features = c(markers_subset_mutant$gene, "PBANKA-1437500"), assay = "RNA")
```
Specifically look for markers between 0 and 2
```{r}
cluster_1_2_markers <- FindMarkers(seurat_bipot, ident.1 = 0, ident.2 = 2, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)

cluster_1_2_markers_subset <- cluster_1_2_markers %>% filter(p_val_adj < 0.05)
cluster_1_2_markers_subset <- cluster_1_2_markers_subset[order(-cluster_1_2_markers_subset$avg_logFC), ]
cluster_1_2_markers_subset
```

```{r}
## this is no longer needed as only wild-type cells are chosen.
## Just check that mutants aren't interfering  with the analysis i.e. the clusters don't just belong to a certain genotype or mutant cell
#table(seurat_bipot$seurat_clusters, seurat_bipot$identity_combined)
```

Do the same analysis on cluster 5 quickly
```{r}
## subset cluster 13 cells
cells_three <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$seurat_clusters == 5, ])

## subset cluster 13
cells_three <- subset(tenx.mutant.integrated.sex, cells = cells_three)

## re-PCA
cells_three <- RunPCA(cells_three, npcs = 30, verbose = FALSE)
ElbowPlot(cells_three, ndims = 30, reduction = "pca")

## recluster
cells_three <- FindNeighbors(cells_three, dims = 1:5)
## first do a c(0.5,1,2) for resolution and then pick a sensible cluster number
cells_three <- FindClusters(cells_three, resolution = 0.5, random.seed = 42, algorithm = 2)
DimPlot(cells_three, reduction = "pca",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") 

## plot cluster resolution = 1
## plot
DimPlot(cells_three, reduction = "DIM_UMAP",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```

```{r}
# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

# PBANKA-1437500 - AP2G - commitment

## find markers
cells_three_markers <- FindAllMarkers(cells_three, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)

## inspect result
markers_subset <- cells_three_markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_logFC) %>% filter(p_val_adj < 0.05)
markers_subset <- markers_subset[order(-markers_subset$avg_logFC), ]
markers_subset

## find markers
markers_subset_mutant <- markers_subset[which(markers_subset$gene %in% list_of_mutant_genes), ]
markers_subset_mutant

## plot
VlnPlot(cells_three, features = c(markers_subset_mutant$gene, "PBANKA-1437500"), assay = "RNA")
```

```{r}
markers_subset_annotated <- merge(markers_subset, gene_annotations,  by.x = "gene", by.y = "Gene.ID", all = FALSE)
markers_subset_annotated
```


Cluster 2 contains the male cells as this has md1 and ap2g in it. 

cluster 3 has histone h2b, histone 4, actin 1, so likley contains asexual cells

cluster 0 is potentially female as these genes have female-biased expression in the kasia data

cluster 1 genes were not screened so unknown identity

Then look at the co-expression of AP2-G and MD1
```{r}
cluster_2_cells <- rownames(cells_three@meta.data[cells_three@meta.data$seurat_clusters == "2",])
cells_three_2 <- subset(cells_three, cells = cluster_2_cells)

df_plot <- data.frame(t(data.frame(cells_three_2@assays$RNA[c("PBANKA-1302700", "PBANKA-1437500"), ])))

ggplot(df_plot, aes(PBANKA.1302700, PBANKA.1437500)) + geom_point()

rm(cells_three_2)
```

Do the same on cluster 7 

```{r}
## subset cluster 13 cells
cells_seven <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$seurat_clusters == 7 & tenx.mutant.integrated.sex@meta.data$genotype_combined == "WT", ])

## subset cluster 13
cells_seven <- subset(tenx.mutant.integrated.sex, cells = cells_bipot)
cells_seven

## re-PCA
cells_seven <- RunPCA(cells_seven, npcs = 30, verbose = FALSE)
ElbowPlot(cells_seven, ndims = 30, reduction = "pca")

## recluster
cells_seven <- FindNeighbors(cells_seven, dims = 1:15)
cells_seven <- FindClusters(cells_seven, resolution = 1, random.seed = 42, algorithm = 2)
DimPlot(cells_seven, reduction = "pca",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") 

## plot cluster resolution = 1
## plot
DimPlot(cells_seven, reduction = "DIM_UMAP",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```
```{r}
# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

# PBANKA-1437500 - AP2G - commitment

## find markers
cells_seven_markers <- FindAllMarkers(cells_seven, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)

## inspect result
markers_subset <- cells_seven_markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_logFC) %>% filter(p_val_adj < 0.05)
markers_subset <- markers_subset[order(-markers_subset$avg_logFC), ]
markers_subset

## find markers
markers_subset_mutant <- markers_subset[which(markers_subset$gene %in% list_of_mutant_genes), ]
markers_subset_mutant

## plot
#VlnPlot(cells_seven, features = c(markers_subset_mutant$gene, "PBANKA-1437500"), assay = "RNA")
```

```{r}
# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# 
# BiocManager::install("slingshot")
library(slingshot)
library(RColorBrewer)

## extracts only 10x cells 
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"),])
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X" & !tenx.mutant.integrated.sex@meta.data$seurat_clusters == "7"),])


## make a new Seurat of this
seurat.object <-subset(tenx.mutant.integrated.sex, cells = wt_cells)

## get UMAP coordinates
umap_coords <- seurat.object@reductions[["DIM_UMAP"]]@cell.embeddings
## old code: seurat.object@reductions$umapoptimised_post_repca@cell.embeddings
## new code: seurat.object@reductions[["DIM_UMAP"]]@cell.embeddings

## get clusters
#clusters <- as.list(seurat.object@meta.data$integrated_snn_res.4)
#names(clusters) <- rownames(seurat.object@meta.data)
#clusters <- as.list(clusters)
## cluster using kmeans
## you need to set a seed here to ensure the results are reproducible
set.seed(42)
clusters <- kmeans(umap_coords, centers = 13)$cluster

## plot
## make a nicer plot so we can interpret the clusters
df_plotting <- as.data.frame(cbind(umap_coords, clusters))
## change to character to make it discrete
df_plotting$clusters <- as.character(df_plotting$clusters)
## plot
ggplot(df_plotting, aes(x = DIMUMAP_1, y = DIMUMAP_2, colour = clusters)) + 
  geom_point() + 
  scale_colour_manual(values = rainbow(15)) + 
  theme_classic()


## initialise plot to prevent error
plot.new()

## slingshot to get lineages
lineage_uamp <- getLineages(umap_coords, clusters, start.clus = '5', end.clus = c('3', '16'))

## make a curve through lineage 
crv1 <- getCurves(lineage_uamp)

## set up the colour pal
nb.cols <- 18 
mycolors <- colorRampPalette(brewer.pal(9, "Set1"))(nb.cols)

## join points with line segments and plot
plot(umap_coords, col = mycolors[clusters], asp = 3, pch = 16)
lines(crv1, lwd = 3, col = 'black')
```
Visualise cells:
```{r, fig.height = 10, fig.width = 10}
## define cells
slingshot_results_df <- as.data.frame(slingPseudotime(crv1))
curve_one_cells <- rownames(slingshot_results_df[!is.na(slingshot_results_df$curve1), ])
curve_two_cells <- rownames(slingshot_results_df[!is.na(slingshot_results_df$curve2), ])
curve_three_cells <- rownames(slingshot_results_df[!is.na(slingshot_results_df$curve3), ])
curve_four_cells <- rownames(slingshot_results_df[!is.na(slingshot_results_df$curve4), ])

c1_plot <- DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = curve_one_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

c2_plot <- DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = curve_two_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

c3_plot <- DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = curve_three_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

c4_plot <- DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = curve_four_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

plot_grid(c1_plot, c2_plot, c3_plot, c4_plot, labels = c('curve 1', 'curve 2', 'curve 3', 'curve 4'), label_size = 12, 
                                ncol = 2, 
                                nrow=2
                            
                )
```



Add data to Seurat:
```{r}
## extract data to add to Seurat
## extract clusters
meta_data_to_add_from_slingshot <- data.frame(clusters_k_means_UMAP = clusters)
## Add pseudotimes
# check the length of each branch to see which curve is which using: sum(is.na(as.data.frame(slingPseudotime(crv1))$curve1))
# then inspect using the ggplot2 above to where males are - 
# tail(as.data.frame(slingPseudotime(crv1)), 100)
# tail(meta_data_to_add_from_slingshot, 100)
meta_data_to_add_from_slingshot$PT_Female_UMAP <- as.data.frame(slingPseudotime(crv1))$curve1
meta_data_to_add_from_slingshot$PT_Male_UMAP <- as.data.frame(slingPseudotime(crv1))$curve2
## add designation to SCE object
meta_data_to_add_from_slingshot$sex_UMAP <- "pre-det"
meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Female_UMAP))] <- "male"
meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Male_UMAP))] <- "female"

## if there are 3 curves in slingPseudotime(crv1):
#meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Female_UMAP) & is.na(as.data.frame(slingPseudotime(crv1))$curve3))] <- "male"
#meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Male_UMAP) & is.na(as.data.frame(slingPseudotime(crv1))$curve3))] <- "female"

## add clusters to SCE object
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, meta_data_to_add_from_slingshot)
```

```{r, fig.height = 5, fig.width = 10}
## plot
FeaturePlot(tenx.mutant.integrated.sex, label.size = 5, pt.size = 0.5, features = c("PT_Female_UMAP", "PT_Male_UMAP")) +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```



plot sex designations
```{r, fig.height = 7, fig.width = 7}
DimPlot(tenx.mutant.integrated.sex, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "sex_UMAP", na.value = "white") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```




# 6. Monocle on sex cells {.tabset}

## calculate pseudotime and modules

#### Preparation
```{r}
## extracts only 10x cells and also remove cluster 0 cells 
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X" & !tenx.mutant.integrated.sex@meta.data$seurat_clusters == "7"),])

## make a new Seurat of this
seurat.object <-subset(tenx.mutant.integrated.sex, cells = wt_cells)

## check that this is the same as the pb_sex_filtered object
#data_test <- as(as.matrix(GetAssayData(pb_sex_filtered, assay = "RNA", slot = "data")), 'sparseMatrix')
#is.equal
#is.identical

## extract counts and pheno:
## the reason we use the integrated and then subsetted is because these cells have been normalised whereas the cells in pb_sex_filtered have not been normalised (well they have but with doublets in them)
data <- as(as.matrix(GetAssayData(seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
## make phenodata
pd <- data.frame(seurat.object@meta.data)
## keep only the columns that are relevant in metadata
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
## make gene metadata
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))

## Construct monocle cds
monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)

## preprocess
monocle.object = preprocess_cds(monocle.object, num_dim = 50, norm_method = "none")
### if using integrated data:
# norm_method = "none", alignment_group = "~ experiment"

## plot jack straw plot
#plot_pc_variance_explained(monocle.object)

#monocle.object = reduce_dimension(monocle.object, reduction_method = "UMAP", preprocess_method = "PCA", umap.metric = "euclidean", umap.n_neighbors = 50, umap.min_dist = 0.5, verbose = FALSE)

#plot_cells(monocle.object, color_cells_by="experiment")

## graph learning

## add UMAP from Seurat
monocle.object@int_colData@listData$reducedDims@listData[["UMAP"]] <- seurat.object@reductions[["DIM_UMAP"]]@cell.embeddings
## if you want the old UMAP from the original all cells one, use: "DIM_UMAP"

## cluster
## this is essential to run the learn_graph function later on
monocle.object = cluster_cells(monocle.object)

## plot initial clustering by monocle
#plot_cells(monocle.object, color_cells_by="cluster", group_cells_by="partition", x = 2, y = 1)

## map pseudotime
monocle.object = learn_graph(monocle.object, learn_graph_control=list(ncenter=250, minimal_branch_len = 30), use_partition = FALSE)
# learn_graph_control=list(ncenter=500) - play with this parameter - lower tends to give fewer branches and higher tends to give more
# 500 - old analysis
# 250 - new analysis 

## Plot cells
plot_cells(monocle.object, color_cells_by="partition", group_cells_by="partition", x = 2, y = 1)
```

Define identities of cells 

male
```{r}
monocle.object_male <- choose_graph_segments(monocle.object)
```
female
```{r}
monocle.object_female <- choose_graph_segments(monocle.object)
```
bipotential
```{r}
monocle.object_bipot <- choose_graph_segments(monocle.object)
```
asexual
```{r}
monocle.object_asex <- choose_graph_segments(monocle.object)
```
asexual fate
```{r}
monocle.object_asex_fate <- choose_graph_segments(monocle.object)
```
check
```{r}
df_freq <- data.frame(table(c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))))
paste("number of cells in seurat object is", length(colnames(monocle.object)), ". The number of cells selected here with an identitity is", dim(df_freq)[1])
df_freq <- df_freq[df_freq$Freq > 1, ]
df_freq
```
Inspect where these missing cells are:
```{r}
'%ni%' <- Negate('%in%')

not_assigned_cells <- colnames(monocle.object)[colnames(monocle.object) %ni% c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))]

DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = not_assigned_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))
```

```{r}
## create annotation dataframe from these results:
df_monocle_sexes <- rbind(data.frame("cell_name" = colnames(monocle.object_male), "sex" = rep("Male", length(colnames(monocle.object_male)))),
                          data.frame("cell_name" = colnames(monocle.object_female), "sex" = rep("Female", length(colnames(monocle.object_female)))),
                          data.frame("cell_name" = colnames(monocle.object_bipot), "sex" = rep("Bipotential", length(colnames(monocle.object_bipot)))),
                          data.frame("cell_name" = colnames(monocle.object_asex), "sex" = rep("Asexual", length(colnames(monocle.object_asex)))),
                          data.frame("cell_name" = colnames(monocle.object_asex_fate), "sex" = rep("Asexual_Fate", length(colnames(monocle.object_asex_fate)))),
                          data.frame("cell_name" = not_assigned_cells, "sex" = rep("Unassigned", length(not_assigned_cells)))
                          )

dim(df_monocle_sexes)

## order like the metadata
df_monocle_sexes <- df_monocle_sexes[match(rownames(monocle.object@colData), df_monocle_sexes$cell_name), ]

## add this back into the monocle object
monocle.object@colData$Sexes_monocle <- df_monocle_sexes$sex
```

#### Pseudotime Calculation
```{r}
## Order the cells and calculate pseudotime
monocle.object = order_cells(monocle.object)

## Plot
umap_pt <- plot_cells(monocle.object, color_cells_by = "pseudotime", label_cell_groups=FALSE, cell_size = 1, x = 2, y = 1, label_branch_points=FALSE, label_leaves=FALSE, label_groups_by_cluster=FALSE, label_roots = FALSE) +
  coord_fixed() +
  theme_void() +
  labs(title = "Pseudotime") +
  theme(plot.title = element_text(hjust = 0.5))

## view
umap_pt
```
save
```{r}
ggsave("../images_to_export/GCSKO_sexbranch_umap_pt.png", plot = umap_pt, device = "png", path = NULL, scale = 1, width = 10, height = 10, units = "cm", dpi = 300, limitsize = TRUE)
```

Just check if the 'hook' on the males is a lineage or dead/dying/activated gams, or mutants

```{r}
FeaturePlot(tenx.mutant.integrated.sex, features = "PBANKA-0416100", coord.fixed = TRUE, min.cutoff = "q1", dims = c(2,1), reduction = "DIM_UMAP", pt.size = 1, order = TRUE) + 
            theme_void() + 
            labs(title = paste("MG1 (Male)")) + 
            theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
            scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
```

It appears MG1 marker expression is decreasing/off in these cells - inspect mutant status

```{r, fig.width = 4, fig.height = 4}
plot <- DimPlot(tenx.mutant.integrated.sex, label = FALSE, label.size = 8, repel = FALSE, pt.size = 0.05, dims = c(2,1), reduction = "DIM_UMAP", group.by = "identity_updated") + 
  coord_fixed() +
  theme(legend.position="bottom", 
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank()) + 
  guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))

HoverLocator(plot = plot, information = FetchData(tenx.mutant.integrated.sex, vars = c("ident", "identity_updated", "nFeature_RNA")))
```

#### Check how well it correlates with the original pseudotime

when pseudotime was calculated on the whole object

```{r}
library(ggpubr)
## extract pseudotime values:
pt_values_new <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
pt_values_new$cell_name <- rownames(pt_values_new)
meta_data_df <- as.data.frame(monocle.object@colData)
meta_data_df$cell_name <- rownames(meta_data_df)
meta_data_df <- merge(meta_data_df, pt_values_new, by = "cell_name")
names(meta_data_df)[ncol(meta_data_df)]<- "pt"

ggplot(meta_data_df, aes(x = old_pt_values, y = pt, colour = cluster_colours_figure)) + 
  geom_point() +  
  geom_smooth(method = "lm", se = FALSE) +
  theme_classic() + stat_cor(method = "pearson")
```

This shows very good correlation.

#### Isolate Branches

```{r}
## access the closest principal graph node vertex for each cell and assign it as a column in your colData table using
#colData(monocle.object)$closest_vertex <- monocle.object@principal_graph_aux[["UMAP"]]$pr_graph_cell_proj_closest_vertex[,1]

## plot
#plot_cells(monocle.object, color_cells_by = "closest_vertex", label_cell_groups = FALSE)
```

#### Module Construction
```{r}
## find genes that change as a function of pt:
monocle.object_pr_test_res <- graph_test(monocle.object, neighbor_graph="principal_graph", cores=8)

## find significant genes
## I used 0.05 previously in all cells 
## 2884 w. p = 0.01, 3260 w. p = 0.05
pr_deg_ids <- row.names(subset(monocle.object_pr_test_res, q_value < 0.05))

## collect into modules
gene_module_df_sex <- find_gene_modules(monocle.object[pr_deg_ids,], resolution=c(10^seq(-6,2)), random_seed = 1234)

## how many genes in modules?
dim(gene_module_df_sex)
```

```{r, echo = FALSE}
paste("there are", length(levels(gene_module_df_sex$module)), "modules")
```

## Plot Modules

#### General Module Composition

Make a dataframe to plot by aggregating clusters vs. modules
```{r}
## make cell group df
cell_group_df <- tibble::tibble(cell=row.names(colData(monocle.object)), cell_group=colData(monocle.object)$seurat_clusters)

## make plotting df
agg_mat <- aggregate_gene_expression(monocle.object, gene_module_df_sex, cell_group_df)
```

Find out how many genes there are per total so we can add this to the plot
```{r}
## how many genes per module?
genes_per_module <- as.data.frame(table(gene_module_df_sex$module))
genes_per_module
```

Find out which modules our mutant genes reside in
```{r}
## create a list of our mutant gene IDs
list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

## make a dataframe to convert the gene IDs into actual IDs
df_mutant_ids <- as.data.frame(unique(cbind(tenx.mutant.integrated@meta.data$identity_gene_updated, tenx.mutant.integrated@meta.data$identity_updated, tenx.mutant.integrated@meta.data$identity_name_updated)))[-c(1, 3),]
# remove the "820" bit on 10
df_mutant_ids$V1 <- gsub("_820", "", df_mutant_ids$V1)
# change the underscore (_) to a dash (-) in gene IDs
df_mutant_ids$V1 <- gsub("_", "-", df_mutant_ids$V1)
names(df_mutant_ids) <- c("gene_ID", "mutant_identity", "mutant_identity_2")

## subset modules df to include only mutant gene IDs
df_mutant_gene_modules <- as.data.frame(gene_module_df_sex[which(gene_module_df_sex$id %in% list_of_mutant_genes),])
names(df_mutant_gene_modules)[1] <- "gene_ID"

## merge dataframes
df_mutant_gene_modules <- merge(df_mutant_gene_modules, df_mutant_ids, by = "gene_ID")

## Inspect
df_mutant_gene_modules
```

so modules of interest are:
```{r}
table(df_mutant_gene_modules$module)
```

Which modules do other genes of interest lie in?:
```{r}
## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300 
# ccp2 - "PBANKA-1319500" - female 820
# p25 - "PBANKA-0515000" - female
# p28 - "PBANKA-0514900" - female
# ccp3 - "PBANKA-1035200" - female -  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909122/
# nek4 - "PBANKA-0616700" - female
# ap2-fg - "PBANKA-1415700" - female 
# dozi - "PBANKA-1217700" - female
# MG1 - "PBANKA-0416100" - male 820
# hap2 - "PBANKA-1212600" - male
# MAPK2 - "PBANKA-0933700" - male
# nek1 - "PBANKA-1443000" - male
# cdpk4 - "PBANKA-0615200" - male

## create a list of landmark genes
list_of_landmark_genes <- c("PBANKA-1437500",
                            "PBANKA-0909600",
                            "PBANKA-1034300", 
                            "PBANKA-1319500", 
                            "PBANKA-0515000",
                            "PBANKA-0514900",
                            "PBANKA-1035200",
                            "PBANKA-0616700",
                            "PBANKA-1415700",
                            "PBANKA-1217700",
                            "PBANKA-0416100", 
                            "PBANKA-1212600",
                            "PBANKA-0933700",
                            "PBANKA-1443000",
                            "PBANKA-0615200")

name_of_landmark_genes <- c("AP2-G", 
                            "AP2_poran", 
                            "AP2-G2", 
                            "ccp2", 
                            "p25", 
                            "p28",
                            "ccp3",
                            "nek4",
                            "ap2-fg",
                            "DOZI",
                            "mg1", 
                            "hap2", 
                            "mapk2",
                            "nek1",
                            "cdpk4")

## make a df
name_of_landmark_genes <- data.frame("gene_name" = name_of_landmark_genes, "id" = list_of_landmark_genes)

## make dataframe
df_landmark_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% list_of_landmark_genes),]

## merge dataframes
df_landmark_gene_modules <- merge(df_landmark_gene_modules, name_of_landmark_genes, by = "id")

## inspect
df_landmark_gene_modules
```

enrichment of screen hits in modules
```{r}
## read in screen hits
library("readxl")
screen_hits <- read_excel("../data/Screen/Modules_Clusters_Phenotypes.xlsx")

## get only hits
screen_hits_selected <- screen_hits[which(screen_hits$`Gam phenotype screen` == "Both" | screen_hits$`Gam phenotype screen` == "Females" | screen_hits$`Gam phenotype screen` == "Males"), ] 

## extract gene IDs
gene_hits <- screen_hits_selected$`new gene ID`
## change the underscore to a dash
gene_hits <- gsub("_", "-", gene_hits)

## find out which modules they are in
df_hits_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% gene_hits),]
print("screen hits by module")
table(df_hits_gene_modules$module)

## get the number genes screened in that module
screen_hits_screened <- screen_hits[which(screen_hits$`Gam phenotype screen` == "Both" | screen_hits$`Gam phenotype screen` == "Females" | screen_hits$`Gam phenotype screen` == "Males" | screen_hits$`Gam phenotype screen` == "None" | screen_hits$`Gam phenotype screen` == "male not enough power"), ] 
## extract gene IDs
genes_screened <- screen_hits_screened$`new gene ID`
## change the underscore to a dash
genes_screened <- gsub("_", "-", genes_screened)
## find out which modules they are in
df_screened_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% genes_screened),]
print("total genes screened in this module")
table(df_screened_gene_modules$module)

## make a table with this info
pc_screened <- data.frame(hits = table(df_hits_gene_modules$module), screened =  table(df_screened_gene_modules$module))[,-3]
names(pc_screened) <- c("module", "hits", "screened")
pc_screened$pc <- (pc_screened$hits /pc_screened$screened)*100

## view
pc_screened
```
Further investigation of screen hits
```{r}
## rename df
df_screen_hits_selected <- as.data.frame(screen_hits_selected)
df_screen_hits_selected$'new gene ID' <- gsub("_", "-", df_screen_hits_selected$'new gene ID')
names(df_screen_hits_selected)[2] <- "id"
df_gene_module_df_sex <- as.data.frame(gene_module_df_sex)

## merge dfs together
screen_hits_modules <- merge(df_screen_hits_selected, df_gene_module_df_sex, by = "id")

## view
screen_hits_modules
```

DOZI-regulated genes

Find out how many of the genes in each module has a DOZI-regulated gene

```{r}
DOZI_regulated_genes <-
read.csv(file = "../data/Reference/DOZI_regulated_genes.csv", header = TRUE)

## extract gene IDs
dozi_genes <- DOZI_regulated_genes[DOZI_regulated_genes$DOZI_regulated. == "YES", ]$Gene_ID_PB
## change the underscore to a dash
dozi_genes <- gsub("_", "-", dozi_genes)

## find out which modules they are in
df_dozi_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% dozi_genes),]
print("dozi genes by module")
table(df_dozi_gene_modules$module)
```

#### Visualise module expression

plot out modules
```{r, fig.height = 7, fig.width = 7}
## make aggregated df again so you can edit it
agg_mat <- aggregate_gene_expression(monocle.object, gene_module_df_sex, cell_group_df)

## h clust the aggregated matrix
module_dendro <- hclust(dist(agg_mat))

## use these clusters to reorder the modules
gene_module_df_sex$module <- factor(gene_module_df_sex$module, levels = row.names(agg_mat)[module_dendro$order])

## plot
UMAP_modules <- plot_cells(monocle.object, genes=gene_module_df_sex %>% filter(module %in% c(1:20)),
           cell_size = 2, 
           x = 2, y = 1,
           label_cell_groups=FALSE,
           scale_to_range = TRUE,
           show_trajectory_graph=FALSE) +
                          scale_colour_viridis_c(name = "expression", option = "C", alpha = 1) +
                          coord_fixed() +
                          theme_void() +
                          theme(legend.position = "bottom", strip.text.x = element_text(size = 15))

## view
UMAP_modules
```
save
```{r}
ggsave("../images_to_export/GCSKO_sexbranch_umap_modules.png", plot = UMAP_modules, device = "png", path = NULL, scale = 1, width = 30, height = 30, units = "cm", dpi = 300, limitsize = TRUE)
```

```{r}
## make a dataframe of genes per module
genes_per_module <- as.data.frame(table(gene_module_df_sex$module))

## inspect
genes_per_module
```

```{r, fig.height = 8, fig.width = 12}
## A. Preparation of dataframe

## prepare custom dataframe for all cells by modules:
agg_mat_no_group <- aggregate_gene_expression(monocle.object, gene_module_df_sex, scale_agg_values = TRUE)

## B. annotations

## make an anotation
## add pt to the monocle object
monocle.object@colData$pt <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
## make an annotation dataframe
anno_no_group <- data.frame(monocle.object@colData$cluster_colours_figure, monocle.object@colData$pt, as.factor(monocle.object@colData$Prediction.Spearman._Kasia), row.names = rownames(monocle.object@colData))
names(anno_no_group) <- c("Sex", "Pseudotime", "Real_time_prediction")

## make annotation colours
annotation_colours <- list(Sex = c(Male="#016c00", Female="#a52b1e", Bipotential = "#ffe400", Asexual = "#0052c5"),
                           Pseudotime = plasma(12, direction = 1),
                           Real_time_prediction = c("0" = viridis(8, direction = 1)[1] ,"1" =  viridis(8, direction = 1)[2] ,"4" = viridis(8, direction = 1)[3] ,"6" = viridis(8, direction = 1)[4] ,"8" = viridis(8, direction = 1)[5]  ,"12"  = viridis(8, direction = 1)[6]  ,"18" = viridis(8, direction = 1)[7]  ,"24" = viridis(8, direction = 1)[8]))

## change the order of the cols (cells) in data frame
col.order <- rownames(anno_no_group[with(anno_no_group, order(Sex, Pseudotime)), ])
agg_mat_no_group <- agg_mat_no_group[,col.order]

## reorder the rows (gene modules) in the data frame so they are in pt order
## define the order visually and using the clusters originally produced
row.order <- c("8", "3","4", "15", "2", "18", "11",
               "7", "1", "13",
               "10", "14", "12", "16", "9",
               "5","6", "17",
               "19", "20")
## reorder using new order
agg_mat_no_group <- agg_mat_no_group[row.order, ]

## for cuts in columns - used later to count number of cells in each cat
df_meta <- as.data.frame(monocle.object@colData)
female_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Female"), ])
male_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Male"), ])
bi_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Bipotential"), ])
asex_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Asexual"), ])
rm(df_meta)

## add module and the number of cells to the row
## change names for row names to include "module " at the begining of them
labels.row <- stringr::str_c("Module ", row.names(agg_mat_no_group))
## reorder frequency so that it is matching our matrix
genes_per_module_ordered <- genes_per_module[match(row.names(agg_mat_no_group), genes_per_module$Var1), ]
## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module_ordered$Freq)){
  labels.row[i] <- stringr::str_c(labels.row[i]," (n = " ,genes_per_module_ordered$Freq[i], ")")
}

## C. Plotting

## plot
heatmap_main <- pheatmap::pheatmap(agg_mat_no_group, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   ## others: ward.D2,
                   #clustering_method="complete",
                   show_colnames = FALSE,
                   labels_row = labels.row,
                   fontsize_row = 15,
                   fontsize = 15,
                   gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 6)

## view
heatmap_main
```
save
```{r}
ggsave("../images_to_export/GCSKO_sexbranch_heatmap.png", plot = heatmap_main, device = "png", path = NULL, scale = 1, width = 27, height = 20, units = "cm", dpi = 300, limitsize = TRUE)
```

```{r, fig.height = 8, fig.width = 12}
## A. Preparation of dataframe

## prepare custom dataframe for all cells by modules:
agg_mat_no_group <- aggregate_gene_expression(monocle.object, gene_module_df_sex, scale_agg_values = TRUE)

## B. annotations

## make an anotation
## add pt to the monocle object
monocle.object@colData$pt <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
## make an annotation dataframe
anno_no_group <- data.frame(monocle.object@colData$Sexes_monocle, monocle.object@colData$cluster_colours_figure, monocle.object@colData$pt, as.factor(monocle.object@colData$Prediction.Spearman._Kasia), row.names = rownames(monocle.object@colData))
## add names to this dataframe
names(anno_no_group) <- c("Sexes_monocle", "Sex", "Pseudotime", "Real_time_prediction")
## make the sexes classification a factor so it can be reordered (explaination here: https://rstudio-pubs-static.s3.amazonaws.com/7433_4537ea5073dc4162950abb715f513469.html)
anno_no_group$Sexes_monocle <- factor(anno_no_group$Sexes_monocle, levels = c("Asexual", "Unassigned", "Asexual_Fate", "Bipotential", "Female", "Male"))

## make annotation colours
annotation_colours <- list(Sexes_monocle = c(Male="#016c00", Female="#a52b1e", Bipotential = "#ffe400", Asexual = "#0052c5", Asexual_Fate = "#77A9D8", Unassigned = "#0052c5"),
                           Sex = c(Male="#016c00", Female="#a52b1e", Bipotential = "#ffe400", Asexual = "#0052c5"),
                           Pseudotime = plasma(12, direction = 1),
                           Real_time_prediction = c("0" = viridis(8, direction = 1)[1] ,"1" =  viridis(8, direction = 1)[2] ,"4" = viridis(8, direction = 1)[3] ,"6" = viridis(8, direction = 1)[4] ,"8" = viridis(8, direction = 1)[5]  ,"12"  = viridis(8, direction = 1)[6]  ,"18" = viridis(8, direction = 1)[7]  ,"24" = viridis(8, direction = 1)[8]))

## change the order of the cols (cells) in data frame
col.order <- rownames(anno_no_group[with(anno_no_group, order(Sexes_monocle, Pseudotime)), ])
agg_mat_no_group <- agg_mat_no_group[,col.order]

## reorder the rows (gene modules) in the data frame so they are in pt order
## define the order visually and using the clusters originally produced
row.order <- c("8", "3","4", "15", "2", "18", "11",
               "7", "1", "13",
               "10", "14", "12", "16", "9",
               "5","6", "17",
               "19", "20")
## reorder using new order
agg_mat_no_group <- agg_mat_no_group[row.order, ]

## for cuts in columns - used later to count number of cells in each cat
df_meta <- as.data.frame(monocle.object@colData)
female_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Female"), ])
male_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Male"), ])
bi_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Bipotential"), ])
asex_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Asexual"), ])
asex_fate_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Asexual_Fate"), ])
unassigned_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Unassigned"), ])
rm(df_meta)

## add module and the number of cells to the row
## change names for row names to include "module " at the begining of them
labels.row <- stringr::str_c("Module ", row.names(agg_mat_no_group))
## reorder frequency so that it is matching our matrix
genes_per_module_ordered <- genes_per_module[match(row.names(agg_mat_no_group), genes_per_module$Var1), ]
## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module_ordered$Freq)){
  labels.row[i] <- stringr::str_c(labels.row[i]," (n = " ,genes_per_module_ordered$Freq[i], ")")
}

## C. Plotting

## plot
heatmap_main <- pheatmap::pheatmap(agg_mat_no_group, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = TRUE,
                   ## others: ward.D2,
                   #clustering_method="complete",
                   show_colnames = FALSE,
                   labels_row = labels.row,
                   fontsize_row = 15,
                   fontsize = 15,
                   gaps_col = c(length(asex_cells), length(c(asex_cells,unassigned_cells)), length(c(asex_cells,unassigned_cells, asex_fate_cells)), length(c(asex_cells,unassigned_cells, asex_fate_cells,bi_cells)), length(c(asex_cells,unassigned_cells, asex_fate_cells,bi_cells, female_cells)), length(c(asex_cells,unassigned_cells, asex_fate_cells,bi_cells, female_cells, male_cells))),
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 6)

## view
heatmap_main
```





This is for:
```{r}
## repeat of plot above:
## Plot
umap_pt <- plot_cells(monocle.object, color_cells_by = "pseudotime", label_cell_groups=FALSE, cell_size = 1, x = 2, y = 1, label_branch_points=FALSE, label_leaves=FALSE, label_groups_by_cluster=FALSE, label_roots = FALSE) +
  coord_fixed() +
  theme_void() +
  labs(title = "Pseudotime") +
  theme(plot.title = element_text(hjust = 0.5))

## view
umap_pt
```


#### Visualise the expression of select genes over pseudotime

```{r, fig.height = 8, fig.width = 10}
## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300 
#list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

## define genes to plot
list_of_genes_of_interest <- c("PBANKA-0102400", "PBANKA-0413400", "PBANKA-0716500", "PBANKA-0828000", "PBANKA-0902300", "PBANKA-1144800", "PBANKA-1302700", "PBANKA-1418100", "PBANKA-1435200", "PBANKA-1447900", "PBANKA-1454800", "PBANKA-1437500", "PBANKA-1319500", "PBANKA-0416100", "PBANKA-1034300")
## add names
names_of_genes_of_interest <- c("GCSKO-2", "GCSKO-10", "GCSKO-19", "GCSKO-3", "GCSKO-13", "GCSKO-28", "GCSKO-oom", "GCSKO-17", "GCSKO-20", "GCSKO-29", "GCSKO-21", "AP2-G", "CCP2", "MG1", "AP2-G2")

##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)), name = names_of_genes_of_interest)
## reorder
#genes_of_interest <- genes_of_interest[match(c("AP2-G", "CCP2", "GCSKO-21", "GCSKO-17", "GCSKO-2", "MG1", "GCSKO-20", "GCSKO-3", "GCSKO-oom", "GCSKO-29", "GCSKO-10", "GCSKO-28", "GCSKO-19", "GCSKO-13"), genes_of_interest$name), ]

## prepare custom dataframe for all cells by modules:
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest[,1:2])

## reorder using new order
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,col.order]

## plot
heatmap_plot <- pheatmap::pheatmap(agg_mat_genes_of_interest, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = TRUE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   labels_row = as.character(genes_of_interest[,3]),
                   gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
                   #gaps_row = c(1, 6),
                   cutree_rows = 2,
                   ## trying to fix legend issue here
                   #fontsize_row = 10,
                   #fontsize_col = 3,
                   #cellheight=3, 
                   #cellwidth = 3,
                   legend = TRUE,
                   annotation_legend = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours
                   )

heatmap_plot
```

look at some of the "module" genes now too

```{r, fig.height = 8, fig.width = 10}
## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300 
#list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

## define genes to plot
list_of_genes_of_interest <- c("PBANKA-1454000", "PBANKA-1354000")
## add names
names_of_genes_of_interest <- c("GyrB", "DBR1")

##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)), name = names_of_genes_of_interest)
## reorder
#genes_of_interest <- genes_of_interest[match(c("AP2-G", "CCP2", "GCSKO-21", "GCSKO-17", "GCSKO-2", "MG1", "GCSKO-20", "GCSKO-3", "GCSKO-oom", "GCSKO-29", "GCSKO-10", "GCSKO-28", "GCSKO-19", "GCSKO-13"), genes_of_interest$name), ]

## prepare custom dataframe for all cells by modules:
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest[,1:2])

## reorder using new order
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,col.order]

## plot
heatmap_plot <- pheatmap::pheatmap(agg_mat_genes_of_interest, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = TRUE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   labels_row = as.character(genes_of_interest[,3]),
                   gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
                   #gaps_row = c(1, 6),
                   cutree_rows = 2,
                   ## trying to fix legend issue here
                   #fontsize_row = 10,
                   #fontsize_col = 3,
                   #cellheight=3, 
                   #cellwidth = 3,
                   legend = TRUE,
                   annotation_legend = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours
                   )

heatmap_plot
```





















### Plot specific genes of interest

Side plots

construct new dataframes for the cells from mutants for each sex
```{r}
## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly

## male
## subset out only male and pre determination cells
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$at_sex == "male"), ]
## take forward only smart-seq2
male_cells <- male_cells[which(male_cells$experiment == "mutants"), ]
## get cell names
male_cells <- rownames(male_cells)
## subset Seurat object to contain cells of interest  
male.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = male_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(male.seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(male.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
male.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
male.monocle.object = preprocess_cds(male.monocle.object, num_dim = 50, norm_method = "none")  
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
#male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))  
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, exclude.na = FALSE)

## female
## subset out only male and pre determination cells
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$at_sex == "female"), ]
## take forward only wild-type
female_cells <- female_cells[which(female_cells$experiment == "mutants"), ]
## get cell names
female_cells <- rownames(female_cells)
## subset our cell group df to keep only these cells
#female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## subset Seurat object to contain cells of interest  
female.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = female_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(female.seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(female.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
female.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
female.monocle.object = preprocess_cds(female.monocle.object, num_dim = 50, norm_method = "none")  
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
#female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))  
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, exclude.na = FALSE)
```

male
```{r, fig.height = 8, fig.width = 12}
# male_agg_mat
## reorder using new order
male_agg_mat <- male_agg_mat[row.order, ]

## make an anotation
anno_male <- data.frame(male.monocle.object@colData$at_sex, male.monocle.object@colData$old_pt_values, genotype = male.monocle.object@colData$identity_updated, row.names = rownames(male.monocle.object@colData))
names(anno_male) <- c("sex", "Pseudotime", "genotype")

## make annotation colours
annotation_colours <- list(sex = c(male="#016c00", female="#a52b1e", 'pre-det' = "#0052c5"),
                           Pseudotime = magma(12, direction = 1))

## change the order of the data frame
col.order.male <- rownames(anno_male[with(anno_male, order(genotype, Pseudotime)), ])
male_agg_mat <- male_agg_mat[,col.order.male]

## plot
heatmap_male <- pheatmap::pheatmap(male_agg_mat, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   legend = FALSE,
                   annotation_legend = TRUE,
                   annotation_col = anno_male, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 12)

heatmap_male
```

```{r, fig.height = 8, fig.width = 12}
# female_agg_mat
## reorder using new order
female_agg_mat <- female_agg_mat[row.order, ]

## make an anotation
anno_female <- data.frame(female.monocle.object@colData$at_sex, female.monocle.object@colData$old_pt_values, genotype = female.monocle.object@colData$identity_updated, row.names = rownames(female.monocle.object@colData))
names(anno_female) <- c("sex", "Pseudotime", "genotype")

## make annotation colours
annotation_colours <- list(sex = c(male="#016c00", female="#a52b1e", 'pre-det' = "#0052c5"),
                           Pseudotime = magma(12, direction = 1))

## change the order of the data frame
col.order.female <- rownames(anno_female[with(anno_female, order(genotype, Pseudotime)), ])
female_agg_mat <- female_agg_mat[,col.order.female]

## plot
heatmap_female <- pheatmap::pheatmap(female_agg_mat, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   legend = FALSE,
                   annotation_legend = FALSE,
                   annotation_col = anno_female, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 12)

heatmap_female

## save a pheatmap: https://stackoverflow.com/questions/43051525/how-to-draw-pheatmap-plot-to-screen-and-also-save-to-file
```

side plots with groups of mutant cells

female
```{r}
## make a new grouping for cells based on their identity
mutant_group_female <- data.frame(cell = rownames(female.monocle.object@colData), cell_group = female.monocle.object@colData$identity_updated)

## aggregate expression
female_agg_mat_grouped <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, mutant_group_female, exclude.na = FALSE)

## reorder using new order
female_agg_mat_grouped <- female_agg_mat_grouped[row.order, ]

## plot
pheatmap::pheatmap(female_agg_mat_grouped, 
                   #scale="row",
                   cluster_cols = TRUE,
                   cluster_rows = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = TRUE,
                   legend = FALSE,
                   annotation_legend = FALSE,
                   #annotation_col = anno_female, 
                   #annotation_colors = annotation_colours, 
                   cutree_rows = 12)


```

male
```{r}

```

module 12 inspection

```{r, fig.height = 10, fig.width = 12}
## make a df for module 12 genes
module.12.genes <- gene_module_df_sex[gene_module_df_sex$module == 12, ]$id
module.12.genes <- data.frame(id = module.12.genes, group = module.12.genes)

## prepare custom dataframe for all cells by modules:
agg_mat_module_12 <- aggregate_gene_expression(monocle.object, module.12.genes)

## make an anotation
anno_no_group <- data.frame(monocle.object@colData$sex_UMAP, monocle.object@colData$old_pt_values, row.names = rownames(monocle.object@colData))
names(anno_no_group) <- c("sex", "Pseudotime")

## make annotation colours
annotation_colours <- list(sex = c(male="#016c00", female="#a52b1e", 'pre-det' = "#0052c5"),
                           Pseudotime = magma(12, direction = 1))

## change the order of the data frame
col.order <- c(pre_det_ordered_cells, female_ordered_cells, male_ordered_cells)
agg_mat_module_12 <- agg_mat_module_12[,col.order]

## plot
pheatmap::pheatmap(agg_mat_module_12, 
                   #scale="row",
                   cluster_cols = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours,
                   fontsize_row = 7,
                   cutree_rows = 12)
```
save plot
```{r}
heatmap_module_12 <- pheatmap::pheatmap(agg_mat_module_12, 
                   #scale="row",
                   cluster_cols = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours,
                   fontsize_row = 7,
                   cutree_rows = 12)




```

AP2 Expression

```{r}
## reading table of AP2 genes
ap2_genes_table <- read.delim(file = "~/data/AP2_genes_table.txt", header = TRUE, sep ="\t")

## extract list of genes
ap2_genes_list <- dplyr::pull(ap2_genes_table, Gene.ID)
ap2_genes_list <- gsub("_", "-", ap2_genes_list)

## make a df for genes
ap2_genes_list <- data.frame(id = ap2_genes_list, group = ap2_genes_list)

## prepare custom dataframe for all cells by modules:
agg_mat_ap2s <- aggregate_gene_expression(monocle.object, ap2_genes_list)

## change the order of the data frame
col.order <- c(pre_det_ordered_cells, female_ordered_cells, male_ordered_cells)
agg_mat_ap2s <- agg_mat_ap2s[,col.order]

## plot
pheatmap::pheatmap(agg_mat_ap2s, 
                   #scale="row",
                   cluster_cols = FALSE,
                   clustering_method="complete",
                   show_colnames = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours,
                   fontsize_row = 7,
                   cutree_rows = 3)
```


### Module Analysis 

Read in Kasia's modules:
```{r}
## read in kasia modules:
kasia_clusters <- read.csv(file = "~/data/Modules_Clusters_Phenotypes.csv", header = TRUE)

## change _ to -:
kasia_clusters$new.gene.ID <- gsub("_", "-", kasia_clusters$new.gene.ID)

## filter out genes not in modules gene_module_df_sex:
kasia_clusters_filtered <- kasia_clusters[which(kasia_clusters$new.gene.ID %in% gene_module_df_sex$id), ]

## rename new gene id
names(kasia_clusters_filtered)[2] <- "id"

## merge together
modules_merged_df <- merge(kasia_clusters_filtered, gene_module_df_sex, by = "id")

## look at the enrichment with a dotplot:
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(modules_merged_df$Kasia.Cluster, df_meta_data$identity_combined), margin = 2)) * 100)
```









NOT USED
complex heatmap version

```{r}
## make into matrix to plot
col.order <- agg_mat_all_cells_matrix <- as.matrix(agg_mat_all_cells)
```

make annotation
```{r}
## extract pseudotime values:
pt_values <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
names(pt_values) <- "monocle_pt_sex_wt"
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, pt_values)

## save pt values
write.csv(pt_values, file = "~/data_to_export/pt_values_sex_only.csv")
```

```{r}
library(circlize)
## make the annotation df
## get meta data from seurat object and then subset rows out that are wt
df_anno <- tenx.mutant.integrated.sex@meta.data[which(rownames(tenx.mutant.integrated.sex@meta.data) %in% colnames(agg_mat_all_cells_matrix)), ]
## get only columns of interest:
df_anno <- df_anno[ ,c("sex", "monocle_pt_sex_wt"), drop = FALSE ]

## order annotation
df_anno <- df_anno[with(df_anno, order(sex, monocle_pt_sex_wt)),]

## order cols in the matrix
agg_mat_all_cells_matrix <- agg_mat_all_cells_matrix[ ,match(colnames(agg_mat_all_cells_matrix), rownames(df_anno))]

## make annotation
heatmap_annotation <- HeatmapAnnotation(df = cluster_anno, 
                                        col = list(sex = c(male="#016c00", female="#a52b1e", `pre-det` = "#0052c5"), monocle_pt_sex_wt = colorRamp2(c(1:70), inferno(70)))
                                        )

heatmap_annotation <- HeatmapAnnotation(sex = df_anno$sex, pt = df_anno$monocle_pt_sex_wt)
```

plot
```{r}
## make heatmap
modules_heatmap <- Heatmap(agg_mat_all_cells_matrix,
        column_order = NULL,
        cluster_columns = FALSE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(agg_mat_all_cells_matrix)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")
```

```{r}
## extract counts from 10x object
matrix_tenx_counts <- as.matrix(GetAssayData(pb_sex_filtered, assay = "RNA"))
#nk.raw.data <- as.matrix(GetAssayData(pb_sex_filtered, slot = "counts")[, WhichCells(pbmc, ident = "NK")])

## check it is the same as the merged object RNA slot

## check it is the same as the monocle object
matrix_tenx_counts_monocle <- as.matrix(as.data.frame((monocle.object@assays)))
```

```{r}
## make heatmap
modules_heatmap <- Heatmap(matrix_tenx_counts,
        column_order = NULL,
        cluster_columns = TRUE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(matrix_tenx_counts)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        #bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")
```

#### plot as a function of pseudotime

Now, we will integrate the branch data we produced using slingshot and the pseudotime values to plot this heatmap. 

Monocle3 has a handy function that allows us to aggregate expression of groups of cells called aggregate_gene_expression. 

The code for this is located here: https://github.com/cole-trapnell-lab/monocle3/blob/1a02274209c765fe7a60f533a31b1da3dacf6785/R/cluster_genes.R 

Define the groups of cells
```{r}
## Split cells into groups of sexes
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "female"), ]
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "male"), ]
pre_det_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det"), ]

## inspect range of pt values to determine bin width
hist(female_cells$PT_LineageFemale)
hist(male_cells$PT_LineageMale)
hist(pre_det_cells$PT_LineageFemale)
hist(pre_det_cells$PT_LineageMale)

```

Use a bin width of 2

there will be two objects for the cell_group_df: male branch and female branch. Both will include the pre-det branch

```{r}
## Define male and female branch cells
# male
male_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "male"), ]

male_branch_meta_data <- data.frame(cell_id = rownames(male_branch_meta_data), pt = male_branch_meta_data$PT_LineageMale)

male_cell_group_df <- male_branch_meta_data

#female
female_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "female"), ]

female_branch_meta_data <- data.frame(cell_id = rownames(female_branch_meta_data), pt = female_branch_meta_data$PT_LineageFemale)

female_cell_group_df <- female_branch_meta_data

## what's the range of values for each pt?

range(female_cell_group_df$pt)
range(male_cell_group_df$pt)
```

```{r}
## make bin widths
# make a new col for annotation
female_cell_group_df$pt_bin <- NA
for(i in seq(2,68,2)){
  female_cell_group_df$pt_bin[which(female_cell_group_df$pt < i & female_cell_group_df$pt >= (i-2))] <- i
}

male_cell_group_df$pt_bin <- NA
for(i in seq(2,68,2)){
  male_cell_group_df$pt_bin[which(male_cell_group_df$pt < i & male_cell_group_df$pt >= (i-2))] <- i
}
# then remove old pt values
male_cell_group_df <- male_cell_group_df[ ,-2]
female_cell_group_df <- female_cell_group_df[ ,-2]
```

```{r}
## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly

## male
## subset out only male and pre determination cells
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "male" | tenx.mutant.integrated.sex@meta.data$sex == "pre-det"), ]
## take forward only wild-type
male_cells <- male_cells[which(male_cells$identity_combined == "WT" | male_cells$identity_combined == "WT_10X"), ]
#male_cells <- male_cells[which(male_cells$identity_combined == "WT_10X"), ]
## get cell names
male_cells <- rownames(male_cells)
## subset our cell group df to keep only these cells
male_cell_group_df <- male_cell_group_df[which(male_cell_group_df$cell_id %in% male_cells),]
## subset Seurat object to contain cells of interest  
male.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = male_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(male.seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(male.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
male.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
male.monocle.object = preprocess_cds(male.monocle.object, num_dim = 50, norm_method = "none")  
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))  
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, male_cell_group_df, exclude.na = FALSE)

## female
## subset out only male and pre determination cells
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "female" | tenx.mutant.integrated.sex@meta.data$sex == "pre-det"), ]
## take forward only wild-type
female_cells <- female_cells[which(female_cells$identity_combined == "WT" | female_cells$identity_combined == "WT_10X"), ]
#female_cells <- female_cells[which(female_cells$identity_combined == "WT_10X"), ]
## get cell names
female_cells <- rownames(female_cells)
## subset our cell group df to keep only these cells
female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## subset Seurat object to contain cells of interest  
female.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = female_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(female.seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(female.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
female.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
female.monocle.object = preprocess_cds(female.monocle.object, num_dim = 50, norm_method = "none")  
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))  
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, female_cell_group_df, exclude.na = FALSE)
```

```{r, fig.height = 7, fig.width = 5}
## use these clusters to reorder the modules
male_agg_mat <- male_agg_mat[match(levels(gene_module_df_sex$module), row.names(male_agg_mat)), ]
female_agg_mat <- female_agg_mat[match(levels(gene_module_df_sex$module), row.names(female_agg_mat)), ]

pheatmap::pheatmap(male_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(male_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(male_agg_mat, 
                   scale="none",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(female_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(female_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(female_agg_mat, 
                   scale="none",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

```

ComplexHeatmap version
```{r, fig.height = 10, fig.width = 12}
## pheatmap calculates Z scores for plotting values
scale_matrix_by_cols <- function (x) 
{
    m = apply(x, 1, mean, na.rm = T)
    s = apply(x, 1, sd, na.rm = T)
    return((x - m)/s)
}

## calculate z score by col
female_agg_mat_scaled <- t(as.matrix(scale_matrix_by_cols(t(female_agg_mat))))
male_agg_mat_scaled <- t(as.matrix(scale_matrix_by_cols(t(male_agg_mat))))
## reorder cols
female_agg_mat_scaled <- female_agg_mat_scaled[match(levels(gene_module_df_sex$module), row.names(female_agg_mat_scaled)), ]
male_agg_mat_scaled <- male_agg_mat_scaled[match(levels(gene_module_df_sex$module), row.names(male_agg_mat_scaled)), ]

## reorder based on clusters
genes_per_module <- genes_per_module[match(levels(gene_module_df_sex$module), row.names(genes_per_module)), ]

## change names for row names to include "module " at the begining of them
row.names(female_agg_mat_scaled) <- stringr::str_c("Module ", row.names(female_agg_mat_scaled))
row.names(male_agg_mat_scaled) <- stringr::str_c("Module ", row.names(male_agg_mat_scaled))

## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module$Freq)){
  row.names(female_agg_mat_scaled)[i] <- stringr::str_c(row.names(female_agg_mat_scaled)[i]," (n = " ,genes_per_module$Freq[i], ")")
}
for(i in seq_along(genes_per_module$Freq)){
  row.names(male_agg_mat_scaled)[i] <- stringr::str_c(row.names(male_agg_mat_scaled)[i]," (n = " ,genes_per_module$Freq[i], ")")
}

## add annotation:
#heatmap_annotation <- HeatmapAnnotation(df = cluster_anno,
#                                         col = list(
# Identity = c(Male="#016c00", Female="#a52b1e", Asexual= "#0052c5", Committed = "#f2eb23")),
#                                         annotation_legend_param = list(Median_Pseudotime_of_Cluster = list(direction = "horizontal"), Identity = list(nrow = 1)))

library(ComplexHeatmap)
library(RColorBrewer)
modules_heatmap_female <- Heatmap(female_agg_mat_scaled,
        column_order = NULL,
        #row_order = row.names(female_agg_mat_scaled)[module_dendro$order],
        #clustering_method_rows = "ward.D2",
        #bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

modules_heatmap_male <- Heatmap(male_agg_mat_scaled,
        column_order = NULL,
        #row_order = module_dendro$order,
        #clustering_method_rows = "ward.D2",
        #bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

draw(modules_heatmap_female, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")
draw(modules_heatmap_male, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")

## https://www.biostars.org/p/380544/ 
```

4 bin width 

```{r}
## Define male and female branch cells
# male
male_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "male"), ]

male_branch_meta_data <- data.frame(cell_id = rownames(male_branch_meta_data), pt = male_branch_meta_data$PT_LineageMale)

male_cell_group_df <- male_branch_meta_data

#female
female_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "female"), ]

female_branch_meta_data <- data.frame(cell_id = rownames(female_branch_meta_data), pt = female_branch_meta_data$PT_LineageFemale)

female_cell_group_df <- female_branch_meta_data

## what's the range of values for each pt?

range(female_cell_group_df$pt)
range(male_cell_group_df$pt)
```


```{r}
## make bin widths
# make a new col for annotation
female_cell_group_df$pt_bin <- NA
for(i in seq(4,68,4)){
  female_cell_group_df$pt_bin[which(female_cell_group_df$pt < i & female_cell_group_df$pt >= (i-4))] <- i
}

male_cell_group_df$pt_bin <- NA
for(i in seq(4,68,4)){
  male_cell_group_df$pt_bin[which(male_cell_group_df$pt < i & male_cell_group_df$pt >= (i-4))] <- i
}
# then remove old pt values
male_cell_group_df <- male_cell_group_df[ ,-2]
female_cell_group_df <- female_cell_group_df[ ,-2]
```

```{r}
## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly

## male
## subset our cell group df to keep only these cells
male_cell_group_df <- male_cell_group_df[which(male_cell_group_df$cell_id %in% male_cells),]
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))  
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, male_cell_group_df, exclude.na = FALSE)

## female
## subset out only male and pre determination cells
## subset our cell group df to keep only these cells
female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))  
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, female_cell_group_df, exclude.na = FALSE)
```

```{r, fig.height = 7, fig.width = 5}
## use these clusters to reorder the modules
male_agg_mat <- male_agg_mat[match(levels(gene_module_df_sex$module), row.names(male_agg_mat)), ]
female_agg_mat <- female_agg_mat[match(levels(gene_module_df_sex$module), row.names(female_agg_mat)), ]

pheatmap::pheatmap(male_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(female_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

```



#### expression of modules in mutant cells (side panels)

male
```{r}
## make monocle object with mutants
## extract data
mutant_cells_male <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$genotype_combined == "Mutant" & tenx.mutant.integrated.sex@meta.data$sex == "male"),])

## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = mutant_cells_male)

## make new counts and pheno:
## the reason we use the integrated and then subsetted is because these cells have been normalised whereas the cells in pb_sex_filtered have not been normalised (well they have but with doublets in them)
data <- as(as.matrix(GetAssayData(seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(seurat.object@meta.data)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))

## Construct monocle cds
monocle.object.mutants.male <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)

## preprocess
monocle.object.mutants.male = preprocess_cds(monocle.object.mutants.male, num_dim = 50, norm_method = "none")
### if using integrated data:
# norm_method = "none", alignment_group = "~ experiment"

## make a cell group dataframe for aggregating expression values:

mutant_cell_group_df <- data.frame(cell = row.names(monocle.object.mutants.male@colData), cell_group = monocle.object.mutants.male@colData$identity_updated)

## aggregate expression
mutant_male_agg_mat <- aggregate_gene_expression(monocle.object.mutants.male, gene_module_df_sex, mutant_cell_group_df)
```

plot
```{r, fig.height = 7, fig.width = 5}
mutant_male_agg_mat <- mutant_male_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_male_agg_mat)), ]

pheatmap::pheatmap(mutant_male_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = TRUE) + theme(legend.position = "bottom")
```

```{r, fig.height = 7, fig.width = 5}
mutant_male_agg_mat <- mutant_male_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_male_agg_mat)), ]

pheatmap::pheatmap(mutant_male_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = TRUE) + theme(legend.position = "bottom")
```

female
```{r}
## make monocle object with mutants
## extract data
mutant_cells_female <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$genotype_combined == "Mutant" & tenx.mutant.integrated.sex@meta.data$sex == "female"),])

## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = mutant_cells_female)

## make new counts and pheno:
## the reason we use the integrated and then subsetted is because these cells have been normalised whereas the cells in pb_sex_filtered have not been normalised (well they have but with doublets in them)
data <- as(as.matrix(GetAssayData(seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(seurat.object@meta.data)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))

## Construct monocle cds
monocle.object.mutants.female <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)

## preprocess
monocle.object.mutants.female = preprocess_cds(monocle.object.mutants.female, num_dim = 50, norm_method = "none")
### if using integrated data:
# norm_method = "none", alignment_group = "~ experiment"

## make a cell group dataframe for aggregating expression values:
mutant_cell_group_df <- data.frame(cell = row.names(monocle.object.mutants.female@colData), cell_group = monocle.object.mutants.female@colData$identity_updated)

## aggregate expression
mutant_female_agg_mat <- aggregate_gene_expression(monocle.object.mutants.female, gene_module_df_sex, mutant_cell_group_df)
```

plot
```{r, fig.height = 7, fig.width = 5}
mutant_female_agg_mat <- mutant_female_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_female_agg_mat)), ]

pheatmap::pheatmap(mutant_female_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE) + theme(legend.position = "bottom")
```

```{r, fig.height = 7, fig.width = 5}
mutant_female_agg_mat <- mutant_female_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_female_agg_mat)), ]

pheatmap::pheatmap(mutant_female_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = TRUE) + theme(legend.position = "bottom")
```

#### for particular genes (lower panel)

```{r, fig.height = 2, fig.width = 9}
## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300 
list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

list_of_genes_of_interest <- c("PBANKA-1437500", "PBANKA-0909600","PBANKA-1034300", "PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")
##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)))

## aggregate expression
## make plotting df
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, cell_group_df)

row.names(agg_mat_genes_of_interest) <- genes_of_interest$gene

#row.names(agg_mat_genes_of_interest) <- factor(row.names(agg_mat_genes_of_interest), levels = row.names(agg_mat_genes_of_interest)[module_dendro$order])
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,match(rownames(cluster_anno), colnames(agg_mat_genes_of_interest))]

pheatmap::pheatmap(agg_mat_genes_of_interest, 
                   scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   clustering_method="ward.D2", 
                   annotation_col = cluster_anno, 
                   annotation_colors = annotation_colours)
```

complex heat map

```{r}
## aggregate gene expression
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, df_all_cells)

agg_mat_genes_of_interest <- as.matrix(agg_mat_genes_of_interest)

## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
        column_order = NULL,
        cluster_columns = FALSE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(agg_mat_all_cells_matrix)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")
```

```{r}
## order cols in the matrix
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[ ,match(rownames(df_anno), colnames(agg_mat_genes_of_interest))]

## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
        column_order = NULL,
        cluster_columns = TRUE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(agg_mat_all_cells_matrix)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")
```

Using Seurat to visualise cells
```{r}
# find markers for every cluster compared to all remaining cells, report only the positive ones
tenx.mutant.integrated.sex.markers <- FindAllMarkers(tenx.mutant.integrated.sex, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
tenx.mutant.integrated.sex.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)
```

```{r}
top10 <- tenx.mutant.integrated.sex.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(tenx.mutant.integrated.sex, features = top10$gene) + NoLegend()
```

But we also have the old pt values that we can use in the seurat object ie FeaturePlot(tenx.mutant.integrated.sex, reduction = "pca", pt.size = 0.01, features = "old_pt_values")

So let's plot a heatmap where we plot: (x) all cells vs. (y) genes arranged by module that they belong to. 

add an old pt annotation to the top

prepare data:
```{r}
## extracts only 10x cells 
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"),])

## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = wt_cells)
```

```{r}
DoHeatmap(seurat.object, features = top10$gene) + NoLegend()
```




```{r}
## aggregate gene expression
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, df_all_cells)

agg_mat_genes_of_interest <- as.matrix(agg_mat_genes_of_interest)

## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
        column_order = NULL,
        cluster_columns = FALSE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(agg_mat_all_cells_matrix)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")
```

Expression of CCP2 and MG1 by each genotype and each sex

```{r}
# ccp2 - "PBANKA-1319500" - female 820
# MG1 - "PBANKA-0416100" - male 820

## make a custom dataframe:
marker_820_df <- as.data.frame(t(as.data.frame(tenx.mutant.integrated.sex@assays$RNA@data[c("PBANKA-1319500", "PBANKA-0416100"), ], stringsAsFactors=F)))
marker_820_df$cell_id <- row.names(marker_820_df)
sex_id <- data.frame(sex_at = tenx.mutant.integrated.sex@meta.data$at_sex, genotype = tenx.mutant.integrated.sex@meta.data$identity_updated ,cell_id = row.names(tenx.mutant.integrated.sex@meta.data))
marker_820_df <- merge(marker_820_df, sex_id, by = "cell_id")

ggplot(marker_820_df, aes(fill=sex_at, y=`PBANKA-1319500`, x=genotype)) + 
    geom_violin() +
  geom_jitter(shape=16, position=position_jitter(0.2)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90))
```

```{r}
#tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "female"), ]


VlnPlot(tenx.mutant.integrated.sex, group.by = "identity_updated", split.by = "at_sex", features = c("PBANKA-1319500"), split.plot = TRUE)

VlnPlot(tenx.mutant.integrated.sex, group.by = "identity_updated", split.by = "at_sex", features = c("PBANKA-0416100"), split.plot = TRUE)
```

Differential expression

Re-cluster the data so we have very course grain clusters
```{r}
## find new clusters
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = 1, random.seed = 42, algorithm = 2)

## plot the graph
DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.1") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```

```{r}
VlnPlot(tenx.mutant.integrated.sex, features = c("PT_Female_UMAP", "PT_Male_UMAP"))
```

#### Show representation of genotypes per cluster

prep for dotplot
```{r}
## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)

## define order for plotting 
my_levels_sex <- c("2", "3", "9", "6", "11", "5", "0", "10", "13", "14", "12", "8", "15", "1", "4", "7")

## redefine order of clusters:
df_meta_data$seurat_clusters <- factor(x = df_meta_data$seurat_clusters, levels = my_levels_sex)

## make a new df of CLUSTER and IDENTITY
dot_plot_df <- as.data.frame.matrix(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined))
dot_plot_df$cluster <- rownames(dot_plot_df)

## calculate percentage of cells for each genotype
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined), margin = 2)) * 100)

## make a column for cluster names
dot_plot_df_pc$cluster <- rownames(dot_plot_df_pc)

## melt dataframe for plotting
library(reshape2)
dot_plot_df_pc_melted <- melt(dot_plot_df_pc, variable.name = "cluster")
colnames(dot_plot_df_pc_melted)[2] <- "identity"

## melt the raw number too
dot_plot_df_melted <- melt(dot_plot_df, variable.name = "cluster")
colnames(dot_plot_df_melted)[2] <- "identity"
colnames(dot_plot_df_melted)[3] <- "raw_number"

## merge together
identical(dot_plot_df_melted$cluster, dot_plot_df_pc_melted$cluster)
dot_plot_merged <- cbind(dot_plot_df_melted, dot_plot_df_pc_melted)
dot_plot_merged <- dot_plot_merged[,c(1,2,3,6)]

## redefine order of clusters
dot_plot_merged$cluster <- factor(x = dot_plot_merged$cluster, levels = my_levels_sex)

## where values are zero, add NA
## find wells where it's zero
zero_values <- dot_plot_merged$value == 0
dot_plot_merged$value[zero_values] <- NA

## also do for raw number
zero_values <- dot_plot_merged$raw_number == 0
dot_plot_merged$raw_number[zero_values] <- NA

## reorder x axis:
my_levels_genotype <- c("GCSKO-oom", "GCSKO-29", "GCSKO-2", "GCSKO-19", "GCSKO-3", "GCSKO-21", "GCSKO-13", "GCSKO-28", "GCSKO-10_820", "GCSKO-17", "GCSKO-20", "WT", "WT_10X")

dot_plot_merged$identity <- factor(x = dot_plot_merged$identity, levels = my_levels_genotype)
```

plot
```{r, fig.width = 6, fig.height= 7}
## plot
dot_plot_identity <- ggplot(dot_plot_merged, aes(y = factor(cluster), x = factor(identity))) +
      ## make into a dot plot
      geom_point(aes(colour=value, size=raw_number)) + 
      scale_color_gradient(low="blue", high="red", limits=c( 0, max(dot_plot_df_pc_melted$value)), na.value="white") +
      #change the colours
      scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white") +
      theme_classic() +
      theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), text=element_text(size=16,  family="Arial")) +
      ylab("Cluster") +
      xlab("Identity") +
      theme(axis.text.x=element_text(size=12, angle=45, hjust=1, vjust=1), axis.text.y=element_text(size=12,), legend.position = "bottom", plot.margin = unit(c(1,3,1,3), "lines")) +
    ## annotate males
    geom_hline(aes(yintercept = 2.5)) +
    ## annotate females
    geom_hline(aes(yintercept = 7.5)) +
    ## annotate pheno 1
    geom_vline(aes(xintercept = 4.5)) +
    ## annotate pheno 2
    geom_vline(aes(xintercept = 5.5)) +    
    ## annotate pheno 3
    geom_vline(aes(xintercept = 7.5)) +
    ## annotate pheno 4
    geom_vline(aes(xintercept = 11.5))

## print
print(dot_plot_identity)
```




cut off and plot
```{r}
pre_branch_cells <- c(2,3)
inmature_male_cells <- c()
inmature_female_cells <-
mature_male_cells <- 
mature_female_cells <- 

## plot the graph
DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.1") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom")
```



```{r}
wt_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT" | tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"), ])

male_inmature_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT" | tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"), ])



early_wt_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT" && tenx.mutant.integrated.sex@meta.data$PT_LineageFemale < 46), ])

FindMarkers(tenx.mutant.integrated.sex, cells.1 = , cells.2 = )
```

### Generate New Clusters

We must now recalculate the clusters to gain a better understanding of the heterogeneity of the subset. Since using the previous clusters, the asexual cells obscured some of the variation. 

```{r}
## copy old clusters
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, tenx.mutant.integrated.sex@meta.data$seurat_clusters, col.name = "pre_sex_clusters")
```

```{r}
## generate new clusters at various resolutions
tenx.mutant.integrated.sex <- FindNeighbors(tenx.mutant.integrated.sex, dims = 1:15)
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = c(2,3,4,5,6), random.seed = 42, algorithm = 2)
```

### Visualise

#### Choose Cluster Resolution

View the clusters at different resolutions to chose the appropraite resolution
```{r}
## plot
DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.2") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.3") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.4") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.5") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.6") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```

Go with 4 clusters
```{r}
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = 4, random.seed = 42, algorithm = 2)

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5) +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```

#### Original UMAP

You can also use the original UMAP projection
```{r, fig.height = 7, fig.width = 7}
DimPlot(tenx.mutant.integrated.sex, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5) +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))
```

#### Representation

look at cluster representation
```{r, echo = FALSE}
## for loop which takes each cluster and makes a list of cells and then plots a highlighted plot and adds it to a list

## make a df of the number of 
n_per_cluster <- as.data.frame(table(tenx.mutant.integrated.sex@meta.data$seurat_clusters))
rownames(n_per_cluster) <- n_per_cluster$Var1
n_per_cluster <- n_per_cluster[,2]

## make a blank list
list_UMAPs_by_cluster <- vector(mode = "list", length = length(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)))

## for loop
for(i in seq_along(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters))){
  ## make a list of cells
  list_of_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$seurat_clusters == levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)[i]), ])
  umap_plot <- DimPlot(tenx.mutant.integrated.sex, label = FALSE, repel = TRUE, pt.size = 0.1, cells.highlight = list_of_cells, reduction = "umapoptimised_post_repca") + 
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e")) + 
  theme_void() + 
  labs(title = paste("cluster", levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)[i], "\n", "(n = ", n_per_cluster[i],")")) + 
  theme(plot.title = element_text(hjust = 0.5), legend.position = "none")
  ## add to the list
  list_UMAPs_by_cluster[[i]] <- umap_plot
}
```

plot
```{r}
## this function writes the next bit of code for you
ploty <- c()
for(i in seq_along(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters))){
  ploty <- paste0(ploty, "list_UMAPs_by_cluster[[", i, "]]", " + ")
}
```

```{r, fig.height = 15, fig.width = 20}
## plot
library(gridExtra)
grid.arrange(list_UMAPs_by_cluster[[1]] , list_UMAPs_by_cluster[[2]] , list_UMAPs_by_cluster[[3]] , list_UMAPs_by_cluster[[4]] , list_UMAPs_by_cluster[[5]] , list_UMAPs_by_cluster[[6]] , list_UMAPs_by_cluster[[7]] , list_UMAPs_by_cluster[[8]] , list_UMAPs_by_cluster[[9]] , list_UMAPs_by_cluster[[10]] , list_UMAPs_by_cluster[[11]] , list_UMAPs_by_cluster[[12]] , list_UMAPs_by_cluster[[13]] , list_UMAPs_by_cluster[[14]] , list_UMAPs_by_cluster[[15]] , list_UMAPs_by_cluster[[16]] , list_UMAPs_by_cluster[[17]] , list_UMAPs_by_cluster[[18]] , list_UMAPs_by_cluster[[19]] , list_UMAPs_by_cluster[[20]] , list_UMAPs_by_cluster[[21]] , list_UMAPs_by_cluster[[22]] , list_UMAPs_by_cluster[[23]] , list_UMAPs_by_cluster[[24]] , list_UMAPs_by_cluster[[25]] , list_UMAPs_by_cluster[[26]] , list_UMAPs_by_cluster[[27]] , list_UMAPs_by_cluster[[28]] , list_UMAPs_by_cluster[[29]] , list_UMAPs_by_cluster[[30]] , list_UMAPs_by_cluster[[31]], ncol = 5)
```


```{r}
## for loop which takes each cluster and makes a list of cells and then plots a highlighted plot and adds it to a list

## make a df of the number of 
n_per_cluster <- as.data.frame(table(tenx.mutant.integrated.sex@meta.data$seurat_clusters))
rownames(n_per_cluster) <- n_per_cluster$Var1
n_per_cluster <- n_per_cluster[,2]

## make a blank list
list_UMAPs_by_cluster <- vector(mode = "list", length = length(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)))

## for loop
for(i in seq_along(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters))){
  ## make a list of cells
  list_of_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$seurat_clusters == levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)[i]), ])
  umap_plot <- DimPlot(tenx.mutant.integrated.sex,  reduction = "umap", label = FALSE, repel = TRUE, pt.size = 0.1, cells.highlight = list_of_cells) + 
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e")) + 
  theme_void() + 
  labs(title = paste("cluster", levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)[i], "\n", "(n = ", n_per_cluster[i],")")) + 
  theme(plot.title = element_text(hjust = 0.5), legend.position = "none")
  ## add to the list
  list_UMAPs_by_cluster[[i]] <- umap_plot
}
```

```{r, fig.height = 15, fig.width = 20}
## plot
grid.arrange(list_UMAPs_by_cluster[[1]] , list_UMAPs_by_cluster[[2]] , list_UMAPs_by_cluster[[3]] , list_UMAPs_by_cluster[[4]] , list_UMAPs_by_cluster[[5]] , list_UMAPs_by_cluster[[6]] , list_UMAPs_by_cluster[[7]] , list_UMAPs_by_cluster[[8]] , list_UMAPs_by_cluster[[9]] , list_UMAPs_by_cluster[[10]] , list_UMAPs_by_cluster[[11]] , list_UMAPs_by_cluster[[12]] , list_UMAPs_by_cluster[[13]] , list_UMAPs_by_cluster[[14]] , list_UMAPs_by_cluster[[15]] , list_UMAPs_by_cluster[[16]] , list_UMAPs_by_cluster[[17]] , list_UMAPs_by_cluster[[18]] , list_UMAPs_by_cluster[[19]] , list_UMAPs_by_cluster[[20]] , list_UMAPs_by_cluster[[21]] , list_UMAPs_by_cluster[[22]] , list_UMAPs_by_cluster[[23]] , list_UMAPs_by_cluster[[24]] , list_UMAPs_by_cluster[[25]] , list_UMAPs_by_cluster[[26]] , list_UMAPs_by_cluster[[27]] , list_UMAPs_by_cluster[[28]] , list_UMAPs_by_cluster[[29]] , list_UMAPs_by_cluster[[30]] , list_UMAPs_by_cluster[[31]], ncol = 5)
```

#### Show correspondance with old clusters (Alluvium plot, Sankey diagram)

```{r}
##This is the set up for this:
## two clusters that differ
table(tenx.mutant.integrated.sex@meta.data$seurat_clusters, tenx.mutant.integrated.sex@meta.data$pre_sex_clusters)
```

```{r}
## hemberg uses gvisSankey in https://github.com/hemberg-lab/scmap/blob/3aa2bb487a80a946469393857cea6a6effc618fb/R/Utils.R code - so maybe update with this?

## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)

df_alluvial <- melt(table(data.frame(full_clusters = df_meta_data$pre_sex_clusters, sex_clusters = df_meta_data$seurat_clusters)))

## load required package
#library(ggalluvial)

## plot
ggplot(df_alluvial, aes(y = value, axis1 = full_clusters, axis2 = sex_clusters)) +
  geom_alluvium(aes(fill = sex_clusters),
                width = 0, knot.pos = 0, reverse = FALSE) +
  guides(fill = FALSE) +
  geom_stratum(width = 1/8, reverse = FALSE) +
  geom_text(stat = "stratum", infer.label = TRUE, reverse = FALSE) +
  scale_x_continuous(breaks = 1:2, labels = c("original cluster", "Sex Cluster")) +
  coord_flip() +
  ggtitle("Cluster Identiity in full dataset vs. sex only") +
    theme_classic()
```

#### Show representation of genotypes per cluster

prep for dotplot
```{r}
## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)

## define order for plotting 
my_levels_sex <- c("0", "9", "28", "2", "29", "16", "22", "23", "15", "21", "30", "4", "3", "18", "7", "8", "6", "20", "12", "26", "13", "5", "11", "25", "1", "17", "10", "24", "14", "27", "19")

## redefine order of clusters:
df_meta_data$seurat_clusters <- factor(x = df_meta_data$seurat_clusters, levels = my_levels_sex)

## make a new df of CLUSTER and IDENTITY
dot_plot_df <- as.data.frame.matrix(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined))
dot_plot_df$cluster <- rownames(dot_plot_df)

## calculate percentage of cells for each genotype
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined), margin = 2)) * 100)

## make a column for cluster names
dot_plot_df_pc$cluster <- rownames(dot_plot_df_pc)

## melt dataframe for plotting
library(reshape2)
dot_plot_df_pc_melted <- melt(dot_plot_df_pc, variable.name = "cluster")
colnames(dot_plot_df_pc_melted)[2] <- "identity"

## melt the raw number too
dot_plot_df_melted <- melt(dot_plot_df, variable.name = "cluster")
colnames(dot_plot_df_melted)[2] <- "identity"
colnames(dot_plot_df_melted)[3] <- "raw_number"

## merge together
identical(dot_plot_df_melted$cluster, dot_plot_df_pc_melted$cluster)
dot_plot_merged <- cbind(dot_plot_df_melted, dot_plot_df_pc_melted)
dot_plot_merged <- dot_plot_merged[,c(1,2,3,6)]

## redefine order of clusters
dot_plot_merged$cluster <- factor(x = dot_plot_merged$cluster, levels = my_levels_sex)

## where values are zero, add NA
## find wells where it's zero
zero_values <- dot_plot_merged$value == 0
dot_plot_merged$value[zero_values] <- NA

## also do for raw number
zero_values <- dot_plot_merged$raw_number == 0
dot_plot_merged$raw_number[zero_values] <- NA

## reorder x axis:
my_levels_genotype <- c("GCSKO-oom", "GCSKO-29", "GCSKO-2", "GCSKO-19", "GCSKO-3", "GCSKO-21", "GCSKO-13", "GCSKO-28", "GCSKO-10_820", "GCSKO-17", "GCSKO-20", "WT", "WT_10X")

dot_plot_merged$identity <- factor(x = dot_plot_merged$identity, levels = my_levels_genotype)
```

plot
```{r, fig.width = 6, fig.height= 7}
## plot
dot_plot_identity <- ggplot(dot_plot_merged, aes(y = factor(cluster), x = factor(identity))) +
      ## make into a dot plot
      geom_point(aes(colour=value, size=raw_number)) + 
      scale_color_gradient(low="blue", high="red", limits=c( 0, max(dot_plot_df_pc_melted$value)), na.value="white") +
      #change the colours
      scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white") +
      theme_classic() +
      theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), text=element_text(size=16,  family="Arial")) +
      ylab("Cluster") +
      xlab("Identity") +
      theme(axis.text.x=element_text(size=12, angle=45, hjust=1, vjust=1), axis.text.y=element_text(size=12,), legend.position = "bottom", plot.margin = unit(c(1,3,1,3), "lines")) +
    ## annotate males
    geom_hline(aes(yintercept = 5.5)) +
    ## annotate females
    geom_hline(aes(yintercept = 20.5)) +
    ## annotate pheno 1
    geom_vline(aes(xintercept = 4.5)) +
    ## annotate pheno 2
    geom_vline(aes(xintercept = 5.5)) +    
    ## annotate pheno 3
    geom_vline(aes(xintercept = 7.5)) +
    ## annotate pheno 4
    geom_vline(aes(xintercept = 11.5))

## print
print(dot_plot_identity)
```
























# Save and Export {.tabset}

save environment
```{r}
## This saves everything in the global environment for easy recall later
#save.image(file = "GCSKO_Sex_Branch_Analysis.RData")
#load(file = "GCSKO_Sex_Branch_Analysis.RData")
```

save modules
```{r}
#gene_module_df_sex
write.csv(gene_module_df_sex, file = "../data_to_export/gene_module_df_sex.csv")
#save(pb_30k_sex_filtered, pb_sex_filtered, file = "Part_2_input.Rdata")
```

Save object(s)
```{r}
## save integrated object to file
#saveRDS(tenx.mutant.integrated.sex, file = "../data_to_export/tenx.mutant.integrated.sex.processed.RDS") 
## restore the object
#tenx.mutant.integrated <- readRDS("../data_to_export/tenx.mutant.integrated.sex.processed.RDS")
```

# Appendix {.tabset}

### Useful info

```{r}
## The tree for monocle is located here:
# monocle.object@principal_graph_aux[["UMAP"]]$dp_mst 
## see more info below in getAnywhere(learn_graph)
## in order to get a plottable dataframe of this:
#test <- as.data.frame(t(as.data.frame(monocle.object@principal_graph_aux[["UMAP"]]$dp_mst)))
#ggplot(test, aes(x = DIMUMAP_1, y = DIMUMAP_2)) + geom_point()

## the curves for slingshot are located here:
#as.data.frame(crv1@curves$curve1$s)
## so a plot can be made by:
#ggplot(as.data.frame(crv1@curves$curve3$s), aes(x= DIMUMAP_1, y = DIMUMAP_2)) + geom_line()
```


### Functions Info

```{r}
Seurat:::DoHeatmap
```

```{r}
monocle:::plot_pseudotime_heatmap
```

```{r}
## to understand how the line is stored
getAnywhere(learn_graph)
```


```{r}
getAnywhere(aggregate_gene_expression)
```

so essentially it first takes the counts matrix and subsets it by your gene groups, then it sums the values - if you specify scale then it will calculate the z score of the transformed dataframe 







## A. Diffusion Map

construct map
```{r}
## construct diffusion map
## http://www.bioconductor.org/packages/devel/bioc/vignettes/slingshot/inst/doc/vignette.html
## input is a transformed expression matrix (genes as cols and cells as rows)
dm <- DiffusionMap(t(as.data.frame(tenx.mutant.integrated.sex@assays$integrated@data)))

## extract meta data for plotting
df_meta_data <- (as.data.frame(tenx.mutant.integrated.sex@meta.data))

## make combined dataframe
rd2 <- as.data.frame(cbind(DC1 = dm$DC1, DC2 = dm$DC2, identity = as.factor(as.character(tenx.mutant.integrated.sex@meta.data$post_integration_clusters))))

## plot
ggplot(rd2, aes(x = DC1, y = DC2, colour = as.character(identity))) + geom_point(size = 1) + 
  theme(axis.ticks.y = element_blank()) + 
  theme_classic()
```







```{r, fig.width = 10, fig.height = 3}
## make a density plot for real time correlations using kasia data
## make an annotation dataframe
anno_real_time <- data.frame(monocle.object@colData$cluster_colours_figure, monocle.object@colData$pt, monocle.object@colData$Prediction.Spearman._Kasia, row.names = rownames(monocle.object@colData))
names(anno_real_time) <- c("sex", "Pseudotime", "real_time")

## change the order of the cols (cells) in data frame
col.order <- rownames(anno_real_time[with(anno_real_time, order(sex, Pseudotime)), ])
anno_real_time <- anno_real_time[col.order,]

## add an "order" col to the dataframe to assist in plotting
anno_real_time$order <- c(1:nrow(anno_real_time))

#### plot density plot x
dens1 <- ggplot(anno_real_time, aes(x = order, fill = as.factor(real_time))) + 
                geom_density(alpha = 0.2) +
                #theme_void() + 
                #theme(legend.position = "none")
                ## add annotations for sex
      geom_rect(data = cluster_anno, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2, fill=Median_Pseudotime_of_Cluster), inherit.aes = FALSE) +
    scale_fill_viridis_c(option = "plasma") +
  ## flip coordinates 
  ## geoms below will use another color scale
    new_scale_fill() +
    geom_rect(data = cluster_anno, mapping=aes(xmin=Bx1, xmax=Bx2, ymin=By1, ymax=By2, fill=Identity), inherit.aes = FALSE) +
    scale_fill_manual(values = c("Male" ="#016c00", "Female"="#a52b1e", "Asexual"= "#0052c5", "Committed" = "#f2eb23"))

dens1

```



### TO PUT BACK IN 

### A. 

Put this in after you have decided what is actually male and female, also add a heatmap version below and figure out expression values (e.g. it's a Z-score at the moment)

```{r, fig.height = 10, fig.width = 10}
    ## subset males and females
## from the merge script:
#male_clusters <- c("22", "18", "9", "16")
#female_clusters <- c("19","24","20","3")

## subset males 
mutant_male_seurat <- subset(mutant_only_seurat, idents = c("22", "18", "9", "16"))

## subset females <- 
mutant_female_seurat <- subset(mutant_only_seurat, idents = c("19","24","20","3"))

   ## mca dataset of core genes

## read in data
df_mca_core_genes <- as.data.frame(read.csv("../data/Reference/mca_core_genes.csv",header = TRUE))

## subset male core genes
mca_male_core_genes <- df_mca_core_genes[which(df_mca_core_genes$Unique_Core == "Male"), ]$feature_symbol
## change underscore to dash
mca_male_core_genes <- gsub("_", "-", mca_male_core_genes)
mca_male_core_genes <- mca_male_core_genes[which(mca_male_core_genes %in% rownames(mutant_male_seurat@assays$RNA@data))]

## subset female core genes
mca_female_core_genes <- df_mca_core_genes[which(df_mca_core_genes$Unique_Core == "Female"), ]$feature_symbol
## change underscore to dash
mca_female_core_genes <- gsub("_", "-", mca_female_core_genes)
mca_female_core_genes <- mca_female_core_genes[which(mca_female_core_genes %in% rownames(mutant_female_seurat@assays$RNA@data))]

    ## plot

# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

## reorder the levels so you can plot the clusters as you wish
#my_levels_male <- c("GCSKO-3", "GCSKO-2", "GCSKO-19", "GCSKO-10_820", "GCSKO-21", "GCSKO-13", "GCSKO-17", "GCSKO-20")
my_levels_male <- c("gd1", "md3", "md4", "md5", "fd1", "fd2", "fd3", "fd4")
#my_levels_female <- c("GCSKO-21", "GCSKO-13", "GCSKO-17", "GCSKO-20", "GCSKO-oom", "GCSKO-29", "GCSKO-2", "GCSKO-19", "GCSKO-10_820")
my_levels_female <- c("gd1", "fd1", "fd2", "fd3", "fd4", "md1", "md2", "md3", "md4", "md5")

## reorder the levels
mutant_male_seurat@meta.data$identity_name_updated <- factor(x = mutant_male_seurat@meta.data$identity_name_updated, levels = my_levels_male)
#mutant_male_seurat@meta.data$identity_updated <- factor(x = mutant_male_seurat@meta.data$identity_updated, levels = my_levels_male)
mutant_female_seurat@meta.data$identity_name_updated <- factor(x = mutant_female_seurat@meta.data$identity_name_updated, levels = my_levels_female)
#mutant_female_seurat@meta.data$identity_updated <- factor(x = mutant_female_seurat@meta.data$identity_updated, levels = my_levels_female)

## plot male
dot_plot_male <- DotPlot(mutant_male_seurat, assay = "RNA", features = mca_male_core_genes, group.by = "identity_name_combined") +
  theme_classic() +
  # change appearance and remove axis elements, and make room for arrows
  theme(axis.text.x = element_text(size=16, angle = 45, hjust=1,vjust=1, family = "Arial"), text=element_text(size=16, family="Arial"), legend.position = "bottom", legend.direction = "horizontal", legend.box = "vertical", plot.title = element_blank(), plot.margin = unit(c(1,3,1,3), "lines")) +
  #change the colours
  scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white", begin = 0, end = 1, direction = 1) +
  ## change x axis label
  labs(x = "MCA Core Genes", y = "Genotype", title = "Expression of Male MCA Core Genes in Male Mutant Cells") +
  ## change label on bottom of plot so we can indicate markers
  #scale_y_discrete(labels = c("gd1", "md3", "md4", "md5", "fd1", "fd2", "fd3", "fd4")) +
  coord_flip()

## view
print(dot_plot_male)


## plot female
dot_plot_female <- DotPlot(mutant_female_seurat, assay = "RNA", features = mca_female_core_genes, group.by = "identity_name_combined") +
  theme_classic() +
  # change appearance and remove axis elements, and make room for arrows
  theme(axis.text.x = element_text(size=16, angle = 45, hjust=1,vjust=1, family = "Arial"), text=element_text(size=16, family="Arial"), legend.position = "bottom", legend.direction = "horizontal", legend.box = "vertical", plot.title = element_blank(), plot.margin = unit(c(1,3,1,3), "lines")) +
  #change the colours
  scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white", begin = 0, end = 1, direction = 1) +
  ## change x axis label
  labs(x = "MCA Core Genes", y = "Genotype", title = "Expression of Female MCA Core Genes in Feale Mutant Cells") +
  ## change label on bottom of plot so we can indicate markers
  #scale_y_discrete(labels = c("fd1", "fd2", "fd3", "fd4","md1", "md2",  "md3", "md4", "md5")) +
coord_flip()

## view
print(dot_plot_female)
```

save
```{r}
ggsave("../images_to_export/GCSKO_sexbranch_dot_male.png", plot = dot_plot_male, device = "png", path = NULL, scale = 1, width = 30, height = 60, units = "cm", dpi = 300, limitsize = TRUE)

ggsave("../images_to_export/GCSKO_sexbranch_dot_female.png", plot = dot_plot_female, device = "png", path = NULL, scale = 1, width = 30, height = 60, units = "cm", dpi = 300, limitsize = TRUE)
```








Pheatmap version of heatmap old cold to make main heatmap (NOT RUN)

Make annotations to add to the heatmap
```{r}
## change names for row names to include "module " at the begining of them
row.names(agg_mat) <- stringr::str_c("Module ", row.names(agg_mat))

## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module$Freq)){
  row.names(agg_mat)[i] <- stringr::str_c(row.names(agg_mat)[i]," (n = " ,genes_per_module$Freq[i], ")")
}

## create annotation of clusters for pheatmap:
cluster_anno <- data.frame(cluster = unique(colData(monocle.object)$seurat_clusters))
row.names(cluster_anno) <- cluster_anno$cluster

## clusters were defined earlier as:
male_clusters <- c("13", "5", "11", "25", "1", "17", "10", "24", "14", "27", "19")
female_clusters <- c("16", "22", "23", "15", "21", "30", "4", "3", "18", "7", "8", "6", "20", "12", "26")
asex_clusters <- c("0", "9", "28", "2", "29")

## add identities to the column
cluster_anno$group <- NA
cluster_anno$group[which(cluster_anno$cluster %in% asex_clusters)] <- "Asexual"
cluster_anno$group[which(cluster_anno$cluster %in% male_clusters)] <- "Male"
cluster_anno$group[which(cluster_anno$cluster %in% female_clusters)] <- "Female"
cluster_anno <- cluster_anno[ , 2, drop = FALSE]
cluster_anno

## add median pseudotime per cluster
## help here:
## https://stackoverflow.com/questions/54360855/calculate-mean-for-column-grouped-by-values-of-two-other-columns
## make subsetted dataframe
df_median_pt <- meta_data_df[ ,c("pt", "seurat_clusters")]
## apply across dataframe to get median
mean.df1 <- tapply(df_median_pt$pt, list(df_median_pt$seurat_clusters), median)
mean.df2 <- as.data.frame(as.table(mean.df1))
names(mean.df2) <- c("seurat_clusters", "pt_Median")
rownames(mean.df2) <- mean.df2$seurat_clusters
## to make each value have the mean in the OG dataframe
#merge(df_median_pt, mean.df2)
## add to annotation dataframe
cluster_anno <- merge(cluster_anno, mean.df2, by=0)

## add rownames to dataframe
rownames(cluster_anno) <- cluster_anno$Row.names
## subset to have only info of interest
cluster_anno <- cluster_anno[,c(2,4)]
names(cluster_anno) <- c("Identity", "Median_Pseudotime_of_Cluster")
```

#### WT cells by modules over pt (central panel)

```{r, fig.height = 10, fig.width = 12}
## make annotation colours
annotation_colours <- list(Identity = c(Male="#016c00", Female="#a52b1e", Asexual= "#0052c5"),
                           Median_Pseudotime_of_Cluster = magma(12, direction = 1))

## reorder the levels
## make df of data
agg_mat_df <- as.data.frame(agg_mat)
## remove levels in my_levels that are not present here - i.e. clusters that are missing because they are not represented in the 10X data
my_levels_10x_data <- my_levels_sex[which(my_levels_sex %in% colnames(agg_mat_df))]
## sort the values
agg_mat_df <- agg_mat_df[ ,(match(my_levels_10x_data, colnames(agg_mat_df)))]

## order 
#cluster_anno <- cluster_anno[(match(my_levels_10x_data, rownames(cluster_anno))), ]

## reorder columns 
## first, order the annotation
cluster_anno <- cluster_anno[with(cluster_anno, order(Identity, Median_Pseudotime_of_Cluster)),]

## remove the NAs from this
cluster_anno <- cluster_anno[complete.cases(cluster_anno),]

agg_mat_df <- agg_mat_df[ ,match(rownames(cluster_anno), colnames(agg_mat_df))]

## plot heatmap
pheatmap::pheatmap(agg_mat_df, 
                   scale="row",
                   cluster_cols = FALSE,
                   clustering_method="ward.D2", 
                   annotation_col = cluster_anno, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 12)

#, gaps_col = c(28,29,37)
```

```{r, fig.height = 7, fig.width = 10}
#row.names(agg_mat) <- factor(row.names(agg_mat), levels = row.names(agg_mat)[module_dendro$order])

## plot heatmap
#pheatmap::pheatmap(agg_mat_df, 
  #                  scale="column",
  #                  cluster_cols = TRUE,
  #                  cluster_rows = module_dendro,
  #                  #clustering_method="ward.D2",
  #                  cutree_rows = 5,
  #                  annotation_col = cluster_anno, 
  #                  annotation_colors = annotation_colours) + 
  # theme(legend.position = "bottom")
```
